hypothesis development and hypotheses

2.4 Developing a Hypothesis

Learning objectives.

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.2 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 4.4 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk, "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

Operationalization

Hypothesis Types

Hypotheses examples.

Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

Forming a question
Performing background research
Creating a hypothesis
Designing an experiment
Collecting data
Analyzing the results
Drawing conclusions
Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you expect to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

Is your hypothesis based on your research on a topic?
Can your hypothesis be tested?
Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

Collect as many observations about a topic or problem as you can.
Evaluate these observations and look for possible causes of the problem.
Create a list of possible explanations that you might want to explore.
After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method , falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that if something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

Does your hypothesis focus on something that you can actually test?
Does your hypothesis include both an independent and dependent variable?
Can you manipulate the variables?
Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the dependent variable if you change the independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

"Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
"Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."
"Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
"Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

"People with high-sugar diets and sedentary activity levels are more likely to develop depression."
"Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

"There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
"There is no difference in scores on a memory recall task between children and adults."
"There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

"People who take St. John's wort supplements will have less anxiety than those who do not."
"Adults will perform better on a memory task than children."
"Children who play first-person shooter games will show higher levels of aggression than children who do not."

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as case studies , naturalistic observations , and surveys are often used when conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually cause another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses . R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:]. Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ? PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies . Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
Education : “Implementing a new teaching method will result in higher student achievement scores.”
Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Developing a Hypothesis

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.3 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274 ↵
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Developing a Hypothesis Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Can J Hosp Pharm
v.67(1); Jan-Feb 2014

Research: Articulating Questions, Generating Hypotheses, and Choosing Study Designs

Introduction.

Articulating a clear and concise research question is fundamental to conducting a robust and useful research study. Although “getting stuck into” the data collection is the exciting part of research, this preparation stage is crucial. Clear and concise research questions are needed for a number of reasons. Initially, they are needed to enable you to search the literature effectively. They will allow you to write clear aims and generate hypotheses. They will also ensure that you can select the most appropriate research design for your study.

This paper begins by describing the process of articulating clear and concise research questions, assuming that you have minimal experience. It then describes how to choose research questions that should be answered and how to generate study aims and hypotheses from your questions. Finally, it describes briefly how your question will help you to decide on the research design and methods best suited to answering it.

TURNING CURIOSITY INTO QUESTIONS

A research question has been described as “the uncertainty that the investigator wants to resolve by performing her study” 1 or “a logical statement that progresses from what is known or believed to be true to that which is unknown and requires validation”. 2 Developing your question usually starts with having some general ideas about the areas within which you want to do your research. These might flow from your clinical work, for example. You might be interested in finding ways to improve the pharmaceutical care of patients on your wards. Alternatively, you might be interested in identifying the best antihypertensive agent for a particular subgroup of patients. Lipowski 2 described in detail how work as a practising pharmacist can be used to great advantage to generate interesting research questions and hence useful research studies. Ideas could come from questioning received wisdom within your clinical area or the rationale behind quick fixes or workarounds, or from wanting to improve the quality, safety, or efficiency of working practice.

Alternatively, your ideas could come from searching the literature to answer a query from a colleague. Perhaps you could not find a published answer to the question you were asked, and so you want to conduct some research yourself. However, just searching the literature to generate questions is not to be recommended for novices—the volume of material can feel totally overwhelming.

Use a research notebook, where you regularly write ideas for research questions as you think of them during your clinical practice or after reading other research papers. It has been said that the best way to have a great idea is to have lots of ideas and then choose the best. The same would apply to research questions!

When you first identify your area of research interest, it is likely to be either too narrow or too broad. Narrow questions (such as “How is drug X prescribed for patients with condition Y in my hospital?”) are usually of limited interest to anyone other than the researcher. Broad questions (such as “How can pharmacists provide better patient care?”) must be broken down into smaller, more manageable questions. If you are interested in how pharmacists can provide better care, for example, you might start to narrow that topic down to how pharmacists can provide better care for one condition (such as affective disorders) for a particular subgroup of patients (such as teenagers). Then you could focus it even further by considering a specific disorder (depression) and a particular type of service that pharmacists could provide (improving patient adherence). At this stage, you could write your research question as, for example, “What role, if any, can pharmacists play in improving adherence to fluoxetine used for depression in teenagers?”

TYPES OF RESEARCH QUESTIONS

Being able to consider the type of research question that you have generated is particularly useful when deciding what research methods to use. There are 3 broad categories of question: descriptive, relational, and causal.

Descriptive

One of the most basic types of question is designed to ask systematically whether a phenomenon exists. For example, we could ask “Do pharmacists ‘care’ when they deliver pharmaceutical care?” This research would initially define the key terms (i.e., describing what “pharmaceutical care” and “care” are), and then the study would set out to look for the existence of care at the same time as pharmaceutical care was being delivered.

When you know that a phenomenon exists, you can then ask description and/or classification questions. The answers to these types of questions involve describing the characteristics of the phenomenon or creating typologies of variable subtypes. In the study above, for example, you could investigate the characteristics of the “care” that pharmacists provide. Classifications usually use mutually exclusive categories, so that various subtypes of the variable will have an unambiguous category to which they can be assigned. For example, a question could be asked as to “what is a pharmacist intervention” and a definition and classification system developed for use in further research.

When seeking further detail about your phenomenon, you might ask questions about its composition. These questions necessitate deconstructing a phenomenon (such as a behaviour) into its component parts. Within hospital pharmacy practice, you might be interested in asking questions about the composition of a new behavioural intervention to improve patient adherence, for example, “What is the detailed process that the pharmacist implicitly follows during delivery of this new intervention?”

After you have described your phenomena, you may then be interested in asking questions about the relationships between several phenomena. If you work on a renal ward, for example, you may be interested in looking at the relationship between hemoglobin levels and renal function, so your question would look something like this: “Are hemoglobin levels related to level of renal function?” Alternatively, you may have a categorical variable such as grade of doctor and be interested in the differences between them with regard to prescribing errors, so your research question would be “Do junior doctors make more prescribing errors than senior doctors?” Relational questions could also be asked within qualitative research, where a detailed understanding of the nature of the relationship between, for example, the gender and career aspirations of clinical pharmacists could be sought.

Once you have described your phenomena and have identified a relationship between them, you could ask about the causes of that relationship. You may be interested to know whether an intervention or some other activity has caused a change in your variable, and your research question would be about causality. For example, you may be interested in asking, “Does captopril treatment reduce blood pressure?” Generally, however, if you ask a causality question about a medication or any other health care intervention, it ought to be rephrased as a causality–comparative question. Without comparing what happens in the presence of an intervention with what happens in the absence of the intervention, it is impossible to attribute causality to the intervention. Although a causality question would usually be answered using a comparative research design, asking a causality–comparative question makes the research design much more explicit. So the above question could be rephrased as, “Is captopril better than placebo at reducing blood pressure?”

The acronym PICO has been used to describe the components of well-crafted causality–comparative research questions. 3 The letters in this acronym stand for Population, Intervention, Comparison, and Outcome. They remind the researcher that the research question should specify the type of participant to be recruited, the type of exposure involved, the type of control group with which participants are to be compared, and the type of outcome to be measured. Using the PICO approach, the above research question could be written as “Does captopril [ intervention ] decrease rates of cardiovascular events [ outcome ] in patients with essential hypertension [ population ] compared with patients receiving no treatment [ comparison ]?”

DECIDING WHETHER TO ANSWER A RESEARCH QUESTION

Just because a question can be asked does not mean that it needs to be answered. Not all research questions deserve to have time spent on them. One useful set of criteria is to ask whether your research question is feasible, interesting, novel, ethical, and relevant. 1 The need for research to be ethical will be covered in a later paper in the series, so is not discussed here. The literature review is crucial to finding out whether the research question fulfils the remaining 4 criteria.

Conducting a comprehensive literature review will allow you to find out what is already known about the subject and any gaps that need further exploration. You may find that your research question has already been answered. However, that does not mean that you should abandon the question altogether. It may be necessary to confirm those findings using an alternative method or to translate them to another setting. If your research question has no novelty, however, and is not interesting or relevant to your peers or potential funders, you are probably better finding an alternative.

The literature will also help you learn about the research designs and methods that have been used previously and hence to decide whether your potential study is feasible. As a novice researcher, it is particularly important to ask if your planned study is feasible for you to conduct. Do you or your collaborators have the necessary technical expertise? Do you have the other resources that will be needed? If you are just starting out with research, it is likely that you will have a limited budget, in terms of both time and money. Therefore, even if the question is novel, interesting, and relevant, it may not be one that is feasible for you to answer.

GENERATING AIMS AND HYPOTHESES

All research studies should have at least one research question, and they should also have at least one aim. As a rule of thumb, a small research study should not have more than 2 aims as an absolute maximum. The aim of the study is a broad statement of intention and aspiration; it is the overall goal that you intend to achieve. The wording of this broad statement of intent is derived from the research question. If it is a descriptive research question, the aim will be, for example, “to investigate” or “to explore”. If it is a relational research question, then the aim should state the phenomena being correlated, such as “to ascertain the impact of gender on career aspirations”. If it is a causal research question, then the aim should include the direction of the relationship being tested, such as “to investigate whether captopril decreases rates of cardiovascular events in patients with essential hypertension, relative to patients receiving no treatment”.

The hypothesis is a tentative prediction of the nature and direction of relationships between sets of data, phrased as a declarative statement. Therefore, hypotheses are really only required for studies that address relational or causal research questions. For the study above, the hypothesis being tested would be “Captopril decreases rates of cardiovascular events in patients with essential hypertension, relative to patients receiving no treatment”. Studies that seek to answer descriptive research questions do not test hypotheses, but they can be used for hypothesis generation. Those hypotheses would then be tested in subsequent studies.

CHOOSING THE STUDY DESIGN

The research question is paramount in deciding what research design and methods you are going to use. There are no inherently bad research designs. The rightness or wrongness of the decision about the research design is based simply on whether it is suitable for answering the research question that you have posed.

It is possible to select completely the wrong research design to answer a specific question. For example, you may want to answer one of the research questions outlined above: “Do pharmacists ‘care’ when they deliver pharmaceutical care?” Although a randomized controlled study is considered by many as a “gold standard” research design, such a study would just not be capable of generating data to answer the question posed. Similarly, if your question was, “Is captopril better than placebo at reducing blood pressure?”, conducting a series of in-depth qualitative interviews would be equally incapable of generating the necessary data. However, if these designs are swapped around, we have 2 combinations (pharmaceutical care investigated using interviews; captopril investigated using a randomized controlled study) that are more likely to produce robust answers to the questions.

The language of the research question can be helpful in deciding what research design and methods to use. Subsequent papers in this series will cover these topics in detail. For example, if the question starts with “how many” or “how often”, it is probably a descriptive question to assess the prevalence or incidence of a phenomenon. An epidemiological research design would be appropriate, perhaps using a postal survey or structured interviews to collect the data. If the question starts with “why” or “how”, then it is a descriptive question to gain an in-depth understanding of a phenomenon. A qualitative research design, using in-depth interviews or focus groups, would collect the data needed. Finally, the term “what is the impact of” suggests a causal question, which would require comparison of data collected with and without the intervention (i.e., a before–after or randomized controlled study).

CONCLUSIONS

This paper has briefly outlined how to articulate research questions, formulate your aims, and choose your research methods. It is crucial to realize that articulating a good research question involves considerable iteration through the stages described above. It is very common that the first research question generated bears little resemblance to the final question used in the study. The language is changed several times, for example, because the first question turned out not to be feasible and the second question was a descriptive question when what was really wanted was a causality question. The books listed in the “Further Reading” section provide greater detail on the material described here, as well as a wealth of other information to ensure that your first foray into conducting research is successful.

This article is the second in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous article in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Competing interests: Mary Tully has received personal fees from the UK Renal Pharmacy Group to present a conference workshop on writing research questions and nonfinancial support (in the form of travel and accommodation) from the Dubai International Pharmaceuticals and Technologies Conference and Exhibition (DUPHAT) to present a workshop on conducting pharmacy practice research.

The Craft of Writing a Strong Hypothesis

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

A research hypothesis has to be simple yet clear to look justifiable enough.
It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1. Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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Research Hypothesis: What It Is, Types + How to Develop?

A research hypothesis proposes a link between variables. Uncover its types and the secrets to creating hypotheses for scientific inquiry.

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect.

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

What existing research has been conducted on your chosen topic?
Are there any gaps or unanswered questions in the current literature?
How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

Independent Variable: The variable you manipulate or control.
Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism, run a free check.

Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

The relevant variables
The specific group being studied
The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Research question	Hypothesis	Null hypothesis
What are the health benefits of eating an apple a day?	Increasing apple consumption in over-60s will result in decreasing frequency of doctor’s visits.	Increasing apple consumption in over-60s will have no effect on frequency of doctor’s visits.
Which airlines have the most delays?	Low-cost airlines are more likely to have delays than premium airlines.	Low-cost and premium airlines are equally likely to have delays.
Can flexible work arrangements improve job satisfaction?	Employees who have flexible working hours will report greater job satisfaction than employees who work fixed hours.	There is no relationship between working hour flexibility and job satisfaction.
How effective is secondary school sex education at reducing teen pregnancies?	Teenagers who received sex education lessons throughout secondary school will have lower rates of unplanned pregnancy than teenagers who did not receive any sex education.	Secondary school sex education has no effect on teen pregnancy rates.
What effect does daily use of social media have on the attention span of under-16s?	There is a negative correlation between time spent on social media and attention span in under-16s.	There is no relationship between social media use and attention span in under-16s.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study. Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

Is the language clear and focused?
What is the relationship between your hypothesis and your research topic?
Is your hypothesis testable? If yes, then how?
What are the possible explanations that you might want to explore?
Does your hypothesis include both an independent and dependent variable?
Can you manipulate your variables without hampering the ethical standards?
Does your research predict the relationship and outcome?
Is your research simple and concise (avoids wordiness)?
Is it clear with no ambiguity or assumptions about the readers’ knowledge
Is your research observable and testable results?
Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

There must be a possibility to prove that the hypothesis is true.
There must be a possibility to prove that the hypothesis is false.
The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

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It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

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Overview of the Scientific Method

10 Developing a Hypothesis

Learning objectives.

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.3 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274 ↵
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Knowledge Base

Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a or H 1 ).
Collect data in a way designed to test the hypothesis.
Perform an appropriate statistical test .
Decide whether to reject or fail to reject your null hypothesis.
Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

an estimate of the difference in average height between the two groups.
a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Normal distribution
Descriptive statistics
Measures of central tendency
Correlation coefficient

Methodology

Cluster sampling
Stratified sampling
Types of interviews
Cohort study
Thematic analysis

Research bias

Implicit bias
Cognitive bias
Survivorship bias
Availability heuristic
Nonresponse bias
Regression to the mean

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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Chapter 6 - Hypothesis Development

Chapter 6 overview.

This chapter discusses the third step of the SGAM, highlighted below in gold, Hypothesis Development.

Depiction of correlation betwen knowledge and effort in SGAM, highlighting step 3: hypothesis development

A hypothesis is often defined as an educated guess because it is informed by what you already know about a topic. This step in the process is to identify all hypotheses that merit detailed examination, keeping in mind that there is a distinction between the hypothesis generation and hypothesis evaluation .

If the analysis does not begin with the correct hypothesis, it is unlikely to get the correct answer. Psychological research into how people go about generating hypotheses shows that people are actually rather poor at thinking of all the possibilities. Therefore, at the hypothesis generation stage, it is wise to bring together a group of analysts with different backgrounds and perspectives for a brainstorming session. Brainstorming in a group stimulates the imagination and usually brings out possibilities that individual members of the group had not thought of. Experience shows that initial discussion in the group elicits every possibility, no matter how remote, before judging likelihood or feasibility. Only when all the possibilities are on the table, is the focus on judging them and selecting the hypotheses to be examined in greater detail in subsequent analysis.

When screening out the seemingly improbable hypotheses, it is necessary to distinguish hypotheses that appear to be disproved (i.e., improbable) from those that are simply unproven. For an unproven hypothesis, there is no evidence that it is correct. For a disproved hypothesis, there is positive evidence that it is wrong. Early rejection of unproven, but not disproved, hypotheses biases the analysis, because one does not then look for the evidence that might support them. Unproven hypotheses should be kept alive until they can be disproved. One example of a hypothesis that often falls into this unproven but not disproved category is the hypothesis that an opponent is trying to deceive us. You may reject the possibility of denial and deception because you see no evidence of it, but rejection is not justified under these circumstances. If deception is planned well and properly implemented, one should not expect to find evidence of it readily at hand. The possibility should not be rejected until it is disproved, or, at least, until after a systematic search for evidence has been made, and none has been found.

There is no "correct" number of hypotheses to be considered. The number depends upon the nature of the analytical problem and how advanced you are in the analysis of it. As a general rule, the greater your level of uncertainty, or the greater the impact of your conclusion, the more alternatives you may wish to consider. More than seven hypotheses may be unmanageable; if there are this many alternatives, it may be advisable to group several of them together for your initial cut at the analysis.

Developing Multiple Hypotheses

Developing good hypotheses requires divergent thinking to ensure that all hypotheses are considered. It also requires convergent thinking to ensure that redundant and irrational hypotheses are eliminated. A hypothesis is stated as an "if … then" statement. There are two important qualities about a hypothesis expressed as an "if … then" statement. These are:

Is the hypothesis testable; in other words, could evidence be found to test the validity of the statement?
Is the hypothesis falsifiable; in other words, could evidence reveal that such an idea is not true?

Hypothesis development is ultimately experience-based. In this experienced-based reasoning, new knowledge is compared to previous knowledge. New knowledge is added to this internal knowledge base. Before long, an analyst has developed an internal set of spatial rules. These rules are then used to develop possible hypotheses.

Looking Forward

Developing hypotheses and evidence is the beginning of the sensemaking and Analysis of Competing Hypotheses (ACH) process. ACH is a general purpose intelligence analysis methodology developed by Richards Heuer while he was an analyst at the Central Intelligence Agency (CIA). ACH draws on the scientific method, cognitive psychology, and decision analysis. ACH became widely available when the CIA published Heuer’s The Psychology of Intelligence Analysis . The ACH methodology can help the geospatial analyst overcome cognitive biases common to analysis in national security, law enforcement, and competitive intelligence. ACH forces analysts to disprove hypotheses rather than jump to conclusions and permit biases and mindsets to determine the outcome. ACH is a very logical step-by-step process that has been incorporated into our Structured Geospatial Analytical Method. A complete discussion of ACH is found in Chapter 8 of Heuer’s book.

General Approaches to Problem Solving Utilizing Hypotheses

Science follows at least three general methods of problem solving using hypotheses. These can be called the:

method of the ruling theory
method of the working hypothesis
method of multiple working hypotheses

The first two are the most popular but they can lead to overlooking relevant perspectives, data, and encourage biases. It has been suggested that multiple hypotheses offers a more effective way of overcoming this problem.

Ruling Theories and Working Hypotheses

Our desire to reach an explanation commonly leads us to a tentative interpretation that is based on a single case. The explanation can blind us to other possibilities that we ignored at first glance. This premature explanation can become a ruling theory, and our research becomes focused on proving that ruling theory. The result is a bias to evidence that disproves the ruling theory or supports an alternate explanation. Only if the original hypothesis was by chance correct does our analysis lead to any meaningful intelligence work. The working hypothesis is supposed to be a hypothesis to be tested, not in order to prove the hypothesis, but as a stimulus for study and fact-finding. Nonetheless, the single working hypothesis can become a ruling theory, and the desire to prove the working hypothesis, despite evidence to the contrary, can become as strong as the desire to prove the ruling theory.

Multiple Hypotheses

The method of multiple working hypotheses involves the development, prior to our search for evidence, of several hypotheses that might explain what are attempting to explain. Many of these hypotheses should be contradictory, so that many will prove to be improbable. However, the development of multiple hypotheses prior to the intelligence analysis lets us avoid the trap of the ruling hypothesis and thus makes it more likely that our intelligence work will lead to meaningful results. We open-mindedly envision all the possible explanations of the events, including the possibility that none of the hypotheses are plausible and the possibility that more research and hypothesis development is needed. The method of multiple working hypotheses has several other beneficial effects on intelligence analysis. Human actions are often the result of several factors, not just one, and multiple hypotheses make it more likely that we will see the interaction of the several factors. The beginning with multiple hypotheses also promotes much greater thoroughness than analysis directed toward one hypothesis, leading to analytic lines that we might otherwise overlook, and thus to evidence and insights that might never have been considered. Thirdly, the method makes us much more likely to see the imperfections in our understanding and thus to avoid the pitfall of accepting weak or flawed evidence for one hypothesis when another provides a more possible explanation.

Drawbacks of Multiple Hypotheses

Multiple hypotheses have drawbacks. One is that it is difficult to express multiple hypotheses simultaneously, and therefore there is a natural tendency to favor one. Another problem is developing a large number of hypotheses that can be tested. A third possible problem is that of the indecision that arises as an analyst balances the evidence for various hypotheses, which is likely preferable to the premature rush to a false conclusion.

Actions That Help the Analyst Develop Hypotheses

Action 1: Brainstorming . Begin with a brainstorming session with your knowledge team to identify a set of alternative hypotheses. Focus on the hypotheses that are:

logically consistent with the theories and data uncovered in your grounding;
address the quality and relationships of spaces.

State the hypotheses stated in an "if ... then" format, for example:

If the DC Shooter is a terrorist, then the geospatial pattern of events would be similar to other terrorist acts.
If the DC Shooter is a serial killer, then the geospatial pattern of events would be similar to other serial killers.

Action 2: Review the hypotheses for testability , i.e., can evidence be could found to test the validity of the statement.

Action 3: Check the hypotheses for falsifiability , i.e., could evidence reveal that such an idea is not true.

Action 4: Combine redundant hypotheses.

Action 5:Consider the elimination of improbable and unproven hypotheses.

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How to Write a Hypothesis? Types and Examples

All research studies involve the use of the scientific method, which is a mathematical and experimental technique used to conduct experiments by developing and testing a hypothesis or a prediction about an outcome. Simply put, a hypothesis is a suggested solution to a problem. It includes elements that are expressed in terms of relationships with each other to explain a condition or an assumption that hasn’t been verified using facts. 1 The typical steps in a scientific method include developing such a hypothesis, testing it through various methods, and then modifying it based on the outcomes of the experiments.

A research hypothesis can be defined as a specific, testable prediction about the anticipated results of a study. 2 Hypotheses help guide the research process and supplement the aim of the study. After several rounds of testing, hypotheses can help develop scientific theories. 3 Hypotheses are often written as if-then statements.

Here are two hypothesis examples:

Dandelions growing in nitrogen-rich soils for two weeks develop larger leaves than those in nitrogen-poor soils because nitrogen stimulates vegetative growth. 4

If a company offers flexible work hours, then their employees will be happier at work. 5

What is a hypothesis?
Types of hypotheses
Characteristics of a hypothesis
Functions of a hypothesis
How to write a hypothesis
Hypothesis examples
Frequently asked questions

What is a hypothesis?

A hypothesis expresses an expected relationship between variables in a study and is developed before conducting any research. Hypotheses are not opinions but rather are expected relationships based on facts and observations. They help support scientific research and expand existing knowledge. An incorrectly formulated hypothesis can affect the entire experiment leading to errors in the results so it’s important to know how to formulate a hypothesis and develop it carefully.

A few sources of a hypothesis include observations from prior studies, current research and experiences, competitors, scientific theories, and general conditions that can influence people. Figure 1 depicts the different steps in a research design and shows where exactly in the process a hypothesis is developed. 4

There are seven different types of hypotheses—simple, complex, directional, nondirectional, associative and causal, null, and alternative.

Types of hypotheses

The seven types of hypotheses are listed below: 5 , 6,7

Simple : Predicts the relationship between a single dependent variable and a single independent variable.

Example: Exercising in the morning every day will increase your productivity.

Complex : Predicts the relationship between two or more variables.

Example: Spending three hours or more on social media daily will negatively affect children’s mental health and productivity, more than that of adults.

Directional : Specifies the expected direction to be followed and uses terms like increase, decrease, positive, negative, more, or less.

Example: The inclusion of intervention X decreases infant mortality compared to the original treatment.

Non-directional : Does not predict the exact direction, nature, or magnitude of the relationship between two variables but rather states the existence of a relationship. This hypothesis may be used when there is no underlying theory or if findings contradict prior research.

Example: Cats and dogs differ in the amount of affection they express.

Associative and causal : An associative hypothesis suggests an interdependency between variables, that is, how a change in one variable changes the other.

Example: There is a positive association between physical activity levels and overall health.

A causal hypothesis, on the other hand, expresses a cause-and-effect association between variables.

Example: Long-term alcohol use causes liver damage.

Null : Claims that the original hypothesis is false by showing that there is no relationship between the variables.

Example: Sleep duration does not have any effect on productivity.

Alternative : States the opposite of the null hypothesis, that is, a relationship exists between two variables.

Example: Sleep duration affects productivity.

Characteristics of a hypothesis

So, what makes a good hypothesis? Here are some important characteristics of a hypothesis. 8,9

Testable : You must be able to test the hypothesis using scientific methods to either accept or reject the prediction.
Falsifiable : It should be possible to collect data that reject rather than support the hypothesis.
Logical : Hypotheses shouldn’t be a random guess but rather should be based on previous theories, observations, prior research, and logical reasoning.
Positive : The hypothesis statement about the existence of an association should be positive, that is, it should not suggest that an association does not exist. Therefore, the language used and knowing how to phrase a hypothesis is very important.
Clear and accurate : The language used should be easily comprehensible and use correct terminology.
Relevant : The hypothesis should be relevant and specific to the research question.
Structure : Should include all the elements that make a good hypothesis: variables, relationship, and outcome.

Functions of a hypothesis

The following list mentions some important functions of a hypothesis: 1

Maintains the direction and progress of the research.
Expresses the important assumptions underlying the proposition in a single statement.
Establishes a suitable context for researchers to begin their investigation and for readers who are referring to the final report.
Provides an explanation for the occurrence of a specific phenomenon.
Ensures selection of appropriate and accurate facts necessary and relevant to the research subject.

To summarize, a hypothesis provides the conceptual elements that complete the known data, conceptual relationships that systematize unordered elements, and conceptual meanings and interpretations that explain the unknown phenomena. 1

How to write a hypothesis

Listed below are the main steps explaining how to write a hypothesis. 2,4,5

Make an observation and identify variables : Observe the subject in question and try to recognize a pattern or a relationship between the variables involved. This step provides essential background information to begin your research.

For example, if you notice that an office’s vending machine frequently runs out of a specific snack, you may predict that more people in the office choose that snack over another.

Identify the main research question : After identifying a subject and recognizing a pattern, the next step is to ask a question that your hypothesis will answer.

For example, after observing employees’ break times at work, you could ask “why do more employees take breaks in the morning rather than in the afternoon?”

Conduct some preliminary research to ensure originality and novelty : Your initial answer, which is your hypothesis, to the question is based on some pre-existing information about the subject. However, to ensure that your hypothesis has not been asked before or that it has been asked but rejected by other researchers you would need to gather additional information.

For example, based on your observations you might state a hypothesis that employees work more efficiently when the air conditioning in the office is set at a lower temperature. However, during your preliminary research you find that this hypothesis was proven incorrect by a prior study.

Develop a general statement : After your preliminary research has confirmed the originality of your proposed answer, draft a general statement that includes all variables, subjects, and predicted outcome. The statement could be if/then or declarative.
Finalize the hypothesis statement : Use the PICOT model, which clarifies how to word a hypothesis effectively, when finalizing the statement. This model lists the important components required to write a hypothesis.

P opulation: The specific group or individual who is the main subject of the research

I nterest: The main concern of the study/research question

C omparison: The main alternative group

O utcome: The expected results

T ime: Duration of the experiment

Once you’ve finalized your hypothesis statement you would need to conduct experiments to test whether the hypothesis is true or false.

Hypothesis examples

The following table provides examples of different types of hypotheses. 10 ,11


Null	Hyperactivity is not related to eating sugar.
There is no relationship between height and shoe size.
Alternative	Hyperactivity is positively related to eating sugar.
There is a positive association between height and shoe size.
Simple	Students who eat breakfast perform better in exams than students who don’t eat breakfast.
Reduced screen time improves sleep quality.
Complex	People with high-sugar diet and sedentary activity levels are more likely to develop depression.
Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone.
Directional	As job satisfaction increases, the rate of employee turnover decreases.
Increase in sun exposure increases the risk of skin cancer.
Non-directional	College students will perform differently from elementary school students on a memory task.
Advertising exposure correlates with variations in purchase decisions among consumers.
Associative	Hospitals have more sick people in them than other institutions in society.
Watching TV is related to increased snacking.
Causal	Inadequate sleep decreases memory retention.
Recreational drugs cause psychosis.

Key takeaways

Here’s a summary of all the key points discussed in this article about how to write a hypothesis.

A hypothesis is an assumption about an association between variables made based on limited evidence, which should be tested.
A hypothesis has four parts—the research question, independent variable, dependent variable, and the proposed relationship between the variables.
The statement should be clear, concise, testable, logical, and falsifiable.
There are seven types of hypotheses—simple, complex, directional, non-directional, associative and causal, null, and alternative.
A hypothesis provides a focus and direction for the research to progress.
A hypothesis plays an important role in the scientific method by helping to create an appropriate experimental design.

Frequently asked questions

Hypotheses and research questions have different objectives and structure. The following table lists some major differences between the two. 9


Includes a prediction based on the proposed research	No prediction is made
Designed to forecast the relationship of and between two or more variables	Variables may be explored
Closed ended	Open ended, invites discussion
Used if the research topic is well established and there is certainty about the relationship between the variables	Used for new topics that haven’t been researched extensively. The relationship between different variables is less known

Here are a few examples to differentiate between a research question and hypothesis.


What is the effect of eating an apple a day by adults aged over 60 years on the frequency of physician visits?	Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits
What is the effect of flexible or fixed working hours on employee job satisfaction?	Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.
Does drinking coffee in the morning affect employees’ productivity?	Drinking coffee in the morning improves employees’ productivity.

Yes, here’s a simple checklist to help you gauge the effectiveness of your hypothesis. 9 1. When writing a hypothesis statement, check if it: 2. Predicts the relationship between the stated variables and the expected outcome. 3. Uses simple and concise language and is not wordy. 4. Does not assume readers’ knowledge about the subject. 5. Has observable, falsifiable, and testable results.

As mentioned earlier in this article, a hypothesis is an assumption or prediction about an association between variables based on observations and simple evidence. These statements are usually generic. Research objectives, on the other hand, are more specific and dictated by hypotheses. The same hypothesis can be tested using different methods and the research objectives could be different in each case. For example, Louis Pasteur observed that food lasts longer at higher altitudes, reasoned that it could be because the air at higher altitudes is cleaner (with fewer or no germs), and tested the hypothesis by exposing food to air cleaned in the laboratory. 12 Thus, a hypothesis is predictive—if the reasoning is correct, X will lead to Y—and research objectives are developed to test these predictions.

Null hypothesis testing is a method to decide between two assumptions or predictions between variables (null and alternative hypotheses) in a statistical relationship in a sample. The null hypothesis, denoted as H 0 , claims that no relationship exists between variables in a population and any relationship in the sample reflects a sampling error or occurrence by chance. The alternative hypothesis, denoted as H 1 , claims that there is a relationship in the population. In every study, researchers need to decide whether the relationship in a sample occurred by chance or reflects a relationship in the population. This is done by hypothesis testing using the following steps: 13 1. Assume that the null hypothesis is true. 2. Determine how likely the sample relationship would be if the null hypothesis were true. This probability is called the p value. 3. If the sample relationship would be extremely unlikely, reject the null hypothesis and accept the alternative hypothesis. If the relationship would not be unlikely, accept the null hypothesis.

To summarize, researchers should know how to write a good hypothesis to ensure that their research progresses in the required direction. A hypothesis is a testable prediction about any behavior or relationship between variables, usually based on facts and observation, and states an expected outcome.

We hope this article has provided you with essential insight into the different types of hypotheses and their functions so that you can use them appropriately in your next research project.

References

Dalen, DVV. The function of hypotheses in research. Proquest website. Accessed April 8, 2024. https://www.proquest.com/docview/1437933010?pq-origsite=gscholar&fromopenview=true&sourcetype=Scholarly%20Journals&imgSeq=1
McLeod S. Research hypothesis in psychology: Types & examples. SimplyPsychology website. Updated December 13, 2023. Accessed April 9, 2024. https://www.simplypsychology.org/what-is-a-hypotheses.html
Scientific method. Britannica website. Updated March 14, 2024. Accessed April 9, 2024. https://www.britannica.com/science/scientific-method
The hypothesis in science writing. Accessed April 10, 2024. https://berks.psu.edu/sites/berks/files/campus/HypothesisHandout_Final.pdf
How to develop a hypothesis (with elements, types, and examples). Indeed.com website. Updated February 3, 2023. Accessed April 10, 2024. https://www.indeed.com/career-advice/career-development/how-to-write-a-hypothesis
Types of research hypotheses. Excelsior online writing lab. Accessed April 11, 2024. https://owl.excelsior.edu/research/research-hypotheses/types-of-research-hypotheses/
What is a research hypothesis: how to write it, types, and examples. Researcher.life website. Published February 8, 2023. Accessed April 11, 2024. https://researcher.life/blog/article/how-to-write-a-research-hypothesis-definition-types-examples/
Developing a hypothesis. Pressbooks website. Accessed April 12, 2024. https://opentext.wsu.edu/carriecuttler/chapter/developing-a-hypothesis/
What is and how to write a good hypothesis in research. Elsevier author services website. Accessed April 12, 2024. https://scientific-publishing.webshop.elsevier.com/manuscript-preparation/what-how-write-good-hypothesis-research/
How to write a great hypothesis. Verywellmind website. Updated March 12, 2023. Accessed April 13, 2024. https://www.verywellmind.com/what-is-a-hypothesis-2795239
15 Hypothesis examples. Helpfulprofessor.com Published September 8, 2023. Accessed March 14, 2024. https://helpfulprofessor.com/hypothesis-examples/
Editage insights. What is the interconnectivity between research objectives and hypothesis? Published February 24, 2021. Accessed April 13, 2024. https://www.editage.com/insights/what-is-the-interconnectivity-between-research-objectives-and-hypothesis
Understanding null hypothesis testing. BCCampus open publishing. Accessed April 16, 2024. https://opentextbc.ca/researchmethods/chapter/understanding-null-hypothesis-testing/#:~:text=In%20null%20hypothesis%20testing%2C%20this,said%20to%20be%20statistically%20significant

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LITERATURE REVIEW AND HYPOTHESES

It is common to present the literature with supporting articles that are the foundation for your hypotheses—your tentative answer to the research questions stating the relationship between variables (what we already know supports what you believe your hypothesized results will be). Providing definitions of your conceptual variables is needed.

Your lit review should develop a theory. To make a contribution to the literature, your idea needs to be articulated, organized, and connected in a way that suggests new directions for researchers, fills a gap in the lit. Ideas are not a theory, regardless of how original they are. To be a theory, ideas have to be presented with a clear logic and causal relationship among the variables studied.

As stated in Chapter 6, Matching Publication Sources, be sure to match your literature review to that of your target journal. Use the same literature title heading and any subheadings commonly used in the target journal (literature review, conceptual framework, theoretical development and hypotheses, theory and hypotheses). Match paragraph lengths and writing level and format hypotheses exactly like in the target journal. The number of your references should be in the same range as other articles in your target journal, unless it is a very new topic with limited prior research. Again, cite articles from the target journal.

Here are some do’s and don’ts when writing your lit review.

Keywords . Do use keywords when searching for the literature you will include in your review.
Target journal . Do review and emulate the lit reviews of articles you cite, and match the target journal lit reviews. As stated, be sure to cite articles from the journal you will submit your work to.
Hypotheses . Do format your hypotheses in the same way as the target journal articles (Chapter 6 Matching Publication Sources).
Relevant . Do cite all the “relevant” articles that relate to your study. An article is not a dissertation, so don’t reference irrelevant articles.

The above is an excerpt of Dr. Lussier’s book, Publish Don’t Perish . More points for lit review, along with 170+ tips to get published are included.

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A Beginner’s Guide to Hypothesis Testing in Business

Business professionals performing hypothesis testing

30 Mar 2021

Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.

If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.

Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.

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What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”

Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.

Hypothesis Testing in Business

When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.

The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.

As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.

In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.

Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.

2. Significance Level and P-Value

Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.

With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.

In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.

3. One-Sided vs. Two-Sided Testing

When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.

Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.

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4. Sampling

To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.

A survey involves asking a series of questions to a random population sample and recording self-reported responses.

Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.

Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.

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Learn How to Perform Hypothesis Testing

Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.

If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.

Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .

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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

Table of Content

What is Hypothesis?

Hypothesis meaning, characteristics of hypothesis, sources of hypothesis, types of hypothesis, simple hypothesis, complex hypothesis, directional hypothesis, non-directional hypothesis, null hypothesis (h0), alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis, hypothesis examples, simple hypothesis example, complex hypothesis example, directional hypothesis example, non-directional hypothesis example, alternative hypothesis (ha), functions of hypothesis, how hypothesis help in scientific research.

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

It is made using what we already know and have seen, and it’s the basis for scientific research.
A clear guess tells us what we think will happen in an experiment or study.
It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Here are some key characteristics of a hypothesis:

Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Here are some common types of hypotheses:

Non-directional Hypothesis

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Following are the examples of hypotheses based on their types:

Studying more can help you do better on tests.
Getting more sun makes people have higher amounts of vitamin D.
How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.
Drinking more sweet drinks is linked to a higher body weight score.
Too much stress makes people less productive at work.
Drinking caffeine can affect how well you sleep.
People often like different kinds of music based on their gender.
The average test scores of Group A and Group B are not much different.
There is no connection between using a certain fertilizer and how much it helps crops grow.
Patients on Diet A have much different cholesterol levels than those following Diet B.
Exposure to a certain type of light can change how plants grow compared to normal sunlight.
The average smarts score of kids in a certain school area is 100.
The usual time it takes to finish a job using Method A is the same as with Method B.
Having more kids go to early learning classes helps them do better in school when they get older.
Using specific ways of talking affects how much customers get involved in marketing activities.
Regular exercise helps to lower the chances of heart disease.
Going to school more can help people make more money.
Playing violent video games makes teens more likely to act aggressively.
Less clean air directly impacts breathing health in city populations.

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations.

The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology .

The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data , ultimately driving scientific progress through a cycle of testing, validation, and refinement.

Hypothesis – FAQs

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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which description relates to gesell’s theory of development?

Gesell's theory of development is based on the concept of maturation, where development occurs in a predictable and sequential pattern.

Gesell believed that each child has a unique developmental timetable, and that they progress through a series of stages that are determined by their biological and genetic makeup. Gesell's theory emphasizes the role of nature over nurture in development, and suggests that environmental factors play a minimal role in shaping a child's development. According to Gesell, children's development is influenced by both internal and external factors. Internal factors include genetic and biological influences, such as genes, hormones, and brain development. External factors include environmental influences, such as nutrition, health care, and education. Gesell's theory also suggests that children's development is largely predetermined and unfolds according to a fixed timetable. Overall, Gesell's theory of development emphasizes the role of nature over nurture and suggests that children's development is largely predetermined by their genetics and biological makeup.

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John yudkin’s hypothesis: sugar is a major dietary culprit in the development of cardiovascular disease.

1 Department of Immunology, University of Toronto, Toronto, ON, Canada
2 Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada

To date, the risk of developing atherosclerosis has extended beyond Western countries and now affecting individuals from various ethnic backgrounds and age groups. Traditional risk factors of atherosclerosis, such as hypercholesterolemia, has been better controlled than before due to highly effective and inexpensive therapies at lowering plasma cholesterol levels. However, the role of reducing dietary cholesterol intake, as a public healthy strategy, in preventing the occurrence of cardiovascular mortalities has been recently challenged. Indeed, despite our continuous decline of dietary cholesterol intake within the last 50 years, the incidence of cardiovascular mortalities has continued to rise, thus raising the possibility that other dietary factors, such as fructose-containing sugars, are the major culprit. In the 1970s, John Yudkin first proposed that sugar was the predominant dietary factor that underlies the majority of cardiovascular mortalities, yet his hypothesis was dismissed. However, over the last 25 years substantial scientific evidence has been accumulated to support Yudkin’s hypothesis. The objectives of this review are to highlight Yudkin’s significant contribution to nutritional science by reviewing his hypothesis and summarizing the recent advances in our understanding of fructose metabolism. The metabolic consequences of fructose metabolism, such as fructose-induced uricemia, insulin resistance, lipoprotein hyperproduction and chronic inflammation, and how they are linked to atherosclerosis as risk factors will be discussed. Finally, the review will explore areas that warrant future research and raise important considerations that we need to evaluate when designing future studies.

Introduction

Atherosclerosis remains one of the leading causes of worldwide cardiovascular mortalities. Within the past decades, new evidence in the field has spurred novel concepts that significantly alter our views on this illness, particularly our perspectives on the traditional risk factors of atherosclerosis ( 1 ). For instance, the risk factor profile has now shifted from the elevation of low-density lipoprotein (LDL) cholesterol levels to the elevation of triglyceride-rich lipoproteins (TGRL) and inflammatory pathways. This shift is attributed to the effective therapies on lowering plasma cholesterol levels, as well as the rise of favoring high-carbohydrate diet, which is associated with the rise of TGRL levels ( 1 ). Indeed, the intake of dietary fructose in US has drastically increased within the past 50 years, and its overconsumption has been proposed as a major culprit for the rise of many metabolic diseases, such as obesity, type 2 diabetes, atherosclerosis, as well as certain types of neurological dysfunction ( 2 – 6 ). The notion that sugar is the dietary factor that underlies various cardiovascular diseases was first proposed by John Yudkin in the 1970s ( 7 ), yet his hypothesis was dismissed. Fifty years later, there is a substantial body of scientific evidence to support his hypothesis ( 8 ). Our understanding of fructose metabolism has significantly advanced since then, although its mechanistic link to the development of atherosclerosis is only beginning to be appreciated ( 9 , 10 ).

Initially thought to be only metabolized in the liver, recent advances in the metabolism field has now demonstrated that dietary fructose is metabolized first by intestine then followed by the liver ( 11 , 12 ). Specifically, low concentration of fructose is metabolized by the intestine, while high concentration of fructose saturates the intestinal metabolic capacity, thereby allowing its leakage to the liver and colonic microbiota ( 11 ). The intrinsic metabolism of excessive fructose in the liver, but not the intestine, eventually leads to the development of many features of metabolic syndrome, such as hepatic steatosis ( 13 ) and hypertension ( 14 ), which are also risk factors for developing atherosclerosis. Therefore, in this review, I will review John Yudkin’s hypothesis, with the intent of highlighting his significant contribution to nutritional science and summarize the metabolic consequences of excess dietary fructose metabolism and how they are potentially linked to atherosclerosis as risk factors.

John Yudkin’s hypothesis

In the 1960s, the death of the 34th American President Dwight D. Eisenhower from heart failure has sparked enormous research interest in determining the dietary factors that are responsible for inducing cardiovascular mortalities. Among all, two major school of thoughts were most popularized at that time, and they were proposed by Ancel Keys and John Yudkin, respectively. Keys on one hand hypothesized that total and saturated fat was responsible for the cause of cardiovascular diseases ( 15 , 16 ), while Yudkin on the other hand proposed that dietary sugar was responsible ( 7 , 17 – 21 ). Specifically, Yudkin first highlighted in his study in 1957 that the positive correlation between intake of sugar per year with coronary mortality was better than the consumption of total fat, including animal fat, butter fat, vegetable fat and margarine ( 20 ). Similar findings were also found in his subsequent study in the context of diabetic related mortalities ( 21 ). Notably, during this heated debate, the Sugar Research Foundation helped to downplay the evidence that showed sucrose consumption was a significant risk factor for developing heart disease ( 22 ). This eventually contributed to the public dismissal of Yudkin’s research and shaped the American Heart Association (AHA) dietary recommendation to reduce daily cholesterol consumption ( 19 ). For instance, it was recommended that Americans should lower the intake of saturated fats and replace it with mono- and polyunsaturated fats.

As a result of this dietary recommendation, we have observed a significant decline in dietary cholesterol intake in men and women, specifically from 500 mg/day for men and 320 mg/day for women in 1972 ( 23 ), to 348 mg/day for men and 242 mg/day for women in 2018 ( 24 ). Yet, the current incidence of cardiovascular diseases and obesity have continuously risen with no signs of stopping, which directly questions the role that dietary cholesterol plays in contributing to the current development of cardiovascular disease. In fact, this should not be surprising as the Seven Countries study, the infamous study that Keys formulated his hypothesis on, only demonstrated a correlation but not causation between intake of saturated fat and serum cholesterol levels ( 15 ). Indeed, meta-analysis of the effects of reducing saturated fat intake on improving cardiovascular health within the past decades have been conflicting and failed to reach a clear consensus ( 25 – 30 ).

Due to the lack of substantial evidence that demonstrate the beneficial effects of low-cholesterol diets on cardiovascular health, the AHA and the Dietary Guidelines Advisory Board in the United States eventually removed cholesterol as a nutrient of concern in Americans’ diet in 2015 ( 31 ). On the other hand, there is now strong scientific evidence suggesting that sugar (specifically fructose and fructose-containing sugars) is in fact the major culprit, thus supporting Yudkin’s hypothesis. This advocacy was first led by Dr. Robert Lustig ( 8 , 32 ) and the number of studies that investigate how dietary fructose and fructose-containing sugars could worsen cardiovascular health have been continuously growing ( 9 , 10 , 33 ). For instance, recent epidemiological studies have now found that the consumption of total fructose from added sugar, but not from fruits and vegetables, were associated with a higher risk of coronary heart disease (CHD) ( 34 ). Similar results were also shown in individuals who frequently consumed sugar sweetened beverages, where they also had a greater risk of developing CHD when compared to infrequent consumers ( 35 , 36 ). Overall, these studies demonstrate the important role that fructose overconsumption plays in driving the development of CHD, thus reflecting the urgent need to better understand its underlying mechanisms.

The intestinal and hepatic metabolism of fructose

Human consumption of purified fructose alone is relatively rare as it is often consumed with glucose in the form of sucrose, which is a disaccharide composed of one glucose and one fructose molecule. Similarly, human consumption of fructose also takes place in the form of high-fructose corn syrup (HFCS), which is composed of either 42% or 55% of fructose, with the rest being glucose. Nonetheless, upon the ingestion of sucrose or HFCS, the brush border of the small intestine secretes sucrase-isomaltase to cleave them into free glucose and fructose molecules ( 37 , 38 ). Interestingly, sucrase-isomaltase can also utilize a distinct cleavage site to cleave maltose, a disaccharide link by two glucose molecules, into two free glucose molecules ( 37 , 39 ).

Upon the cleavage of sucrose or HFCS into free monosaccharides, fructose and glucose enter the apical side of enterocytes through SLC2A5 (GLUT5), a passive transporter, and sodium-glucose co-transporter 1 (SGLT-1), an active co-transporter, respectively ( 40 – 42 ) ( Figure 1 ). After entry, the metabolism of fructose increases the expression of GLUT5 to enhance the further uptake of fructose. Specifically, past studies have found that fructose metabolism in enterocytes activated carbohydrate-responsive element-binding protein (ChREBP)-dependent transcription of GLUT5 ( 41 , 43 ), followed by increased endosomal tracking of GLUT5 to the apical membrane in a Ras-related protein-in-brain 11a (RAB11a)-dependent manner ( 41 ), which eventually increased the surface expression of GLUT5. Apart from this, fructose absorbance can also be enhanced by the uptake of other molecules, such as glucose, alanine, proline, and glutamine, with glucose being the most potent one ( 44 , 45 ). Mechanistically, it has been proposed that thioredoxin-interacting protein (TXNIP), which is known to regulate glucose homeostasis, interacts with GLUT5 and promotes fructose uptake, thereby linking glucose and fructose metabolism together in enterocyte ( 46 ). However, the precise molecular events that illustrates how TXNIP enhance fructose uptake via interacting with GLUT5 remains to be determined.

Figure 1 . Entry of fructose in enterocytes and hepatocytes. Diagram that illustrates the respective entry of glucose and fructose in enterocytes and hepatocytes. Sucrase-isomaltase breaks down sucrose into free glucose and fructose molecules. Glucose and fructose enter the enterocytes through the apical pole through SGLT-1 and GLUT5, respectively. At the basolateral pole, fructose can leave enterocytes into the portal circulation through GLUT2 and GLUT5, which can then enter hepatocytes through GLUT2 and GLUT8. Green circle denotes free fructose molecules, blue circle denotes free glucose molecules. Created with Biorender.com .

If the amount of fructose and glucose taken up by enterocytes exceed their maximal capacity of metabolism, the excess fructose and glucose molecules will exit through the basolateral side of enterocytes into the portal circulation via GLUT5 and GLUT2, respectively, ( 40 , 47 , 48 ), although it has also been shown that fructose can also exit through GLUT2 ( 49 ). Through the portal circulation, fructose can subsequently enter hepatocytes through GLUT2 ( 50 – 52 ), as well as GLUT8 ( 53 ), but not through GLUT5 as it is not well expressed in hepatocytes ( 52 ). Similar to its metabolism in enterocytes, fructose in hepatocytes is first phosphorylated by ketohexokinase (KHK), specifically by KHK-C, one of the two alternatively spliced isoforms of KHK that is expressed in liver, small intestine, and kidney ( 54 ) ( Figure 2 ). Unlike its other isoform, KHK-A, which differs from KHK-C by one exon, KHK-C has a higher affinity for fructose than KHK-A ( 31 , 55 ). Therefore, upon its phosphorylation by KHK-C, fructose is rapidly converted into fructose-1-phosphate (F1P). Since the activity of KHK is not subjected to feedback inhibition, its phosphorylation of fructose to F1P is unrestricted, and leads to a constitutively decline of ATP pools and rise of ADP and inosine monophosphate (IMP) pools, which ultimately leads to the formation of uric acid and mitochondrial reactive oxygen species (mtROS) ( 12 , 56 ).

Figure 2 . Hepatic metabolism of fructose. Diagram that illustrates key metabolic steps of fructose metabolism in hepatocytes. Firstly, fructose is constitutively phosphorylated by KHK-C into F1P and leads to the depletion of ATP pools. This in turn increases pools of ADP and eventually the production and secretion of uric acid into the circulation. Uric acid can also stimulate oxidative stress in mitochondria and block aconitase activity, leading to citrate accumulation. Secondly, F1P can be converted to GA and DHAP by aldolase, in which GA can be phosphorylated by triose kinase into GA3P. GA3P can participate in glycolysis and converted to pyruvate or combine with DHAP to form F1-6-BP and enter the glucogenesis pathway. Thirdly, fructose-derived pyruvate can be transformed into lactate and secreted into circulation or participate in the TCA cycle and further increases the levels of citrate. Citrate can then be converted back to acetyl-CoA and participate in the generation of fatty acids, although the acetate produced from fructose metabolism in gut microbes can also contribute to the lipogenic pools of acetyl-CoA. Fourthly, F1P-derived DHAP can also be converted to G3P, which when combined with free fatty acids, can then be used to synthesize triglyceride. The synthesis of triglycerides can be stored as lipid droplets or secreted into the circulation as VLDL. Created with Biorender.com .

After F1P is generated, it is converted to glyceraldehyde (GA) and dihydroxyacetone phosphate (DHAP) by aldolase B. GA is then phosphorylated by triose kinase into glyceraldehyde 3-phosphate (GA3P), which is subsequently converted into phosphoenolpyruvate and pyruvate through a series of enzymatic reactions. The production of pyruvate can be converted lactate via lactate dehydrogenase or enter the tricarboxylic acid (TCA) cycle via pyruvate dehydrogenase. Apart from the production of pyruvate, GA can also combine with F1P-derived DHAP to form fructose-1-6-bisphosphate (F1,6-BP) and enter the gluconeogenic pathway in a ChREBP-dependent manner ( 57 , 58 ). Finally, F1P-derived DHAP can also be converted to glycerol-3-phosphate (G3P) by Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), as well as methylglyoxal by methylglyoxal synthase. Methylglyoxal has been shown to further inhibit acetyl-CoA from entering the mitochondria and inhibits AMP-activated protein kinase (AMPK) signaling, thus promoting fatty acid synthesis over oxidation.

Once pyruvate enters the TCA cycle, it specifically raises the levels of citrate due to mtROS-mediated inhibition of aconitase ( 56 ). This increased level of citrate is then converted to acetyl-CoA by ATP Citrate Lyase (ACLY), and eventually malonyl-CoA by Acetyl-CoA carboxylase 1 (ACC1). Malonyl-CoA can be used to synthesize C16 and C18 fatty acids, such as palmitate, by fatty acid synthase (FASN). These fatty acids can be secreted into the circulation, or when conjugated with DHAP-derived G3P, lead to the formation of triglyceride (TG) and secreted into circulation as very low-density lipoproteins (VLDL) or stored as lipid droplets. Apart from this, it is also important to highlight that recent research has shown that acetate secreted by gut microbes post excess fructose metabolism can also contribute to the lipogenic pools of acetyl-CoA in hepatocytes ( 59 ). This process is independent of hepatocyte ACLY activities and suggests that intrinsic fructose metabolism of gut microbes also participates in hepatic lipogenesis.

Finally, the intrinsic metabolism of fructose in hepatocytes also enhances its rate of glycolysis, specifically, through the production of F1P. Although the rate of glycolysis is tightly regulated based on the cellular energy state (ATP levels) at the level of phosphofructokinase (PFK), KHK-C-mediated depletion of ATP levels, as well as uric acid ( 60 ), can trigger the stimulation of PFK activity and thus promotes a high glycolytic flux in hepatocytes. Notably, F1P itself can also allosterically activate glucokinase-regulatory protein and liver-type pyruvate kinase, which both further enhance the uptake of glucose and its metabolism. Taken together, fructose metabolism in hepatocytes can contribute to multiple cellular energetic processes, including increased de novo lipogenesis, glycolysis, and gluconeogenesis. Metabolite tracing experiments with isotopes have supported this and revealed that upon fructose oxidation, 25% is converted to lactate, 15% is converted to glycogen, 50% is converted to glucose, with the rest being converted to triglyceride ( 61 – 64 ).

Fructose-induced uricemia

As previously described, upon excess dietary fructose-containing sugar consumption, the uncontrolled phosphorylation of fructose by KHK-C in hepatocytes will lead to its depletion of ATP and in turn increase the formation of uric acid. The deposition of uric acid from hepatocytes into the circulation can rapidly elevate its levels up to 4 mg/dL ( 65 , 66 ), and extend into the postprandial phase ( 67 ). Interestingly, fructose is the only monosaccharide that induces uricemia and its deleterious effect has been previously demonstrated. For instance, Nakagawa et al. have first demonstrated that fructose-induced uricemia underlie features of metabolic syndrome, such as hypertension, and that lowering the concentration of uric acid by using uricosuric agent or xanthine oxidase inhibitor could confer protective effects ( 14 ).

Hypertension and uricemia are both key risk factors for atherosclerosis ( 68 – 70 ), and fructose-induced uricemia has been long-associated with hypertension ( 71 , 72 ), most likely through inducing endothelial dysfunction. It is well established that nitric oxide (NO) produced by eNOS in endothelial cells is critical for maintaining blood pressure by inducing smooth muscle cells dilation. However, in the context of hyperuricemia, uric acid can inhibit eNOS-derived NO production levels in both in vitro and in vivo models ( 73 , 74 ), thereby contributing to hypertension. Mechanistically, uric acid activates NADPH-mediated oxidative stress in endothelial cells, and that the production of reactive oxygen species (ROS) impairs the function of eNOS and thus its production of NO ( 75 , 76 ). Apart from inducing oxidative stress, uric acid can also inhibit eNOS function by inducing the expression of C-reactive proteins (CRP). Specifically, a study performed by Kang et al. have shown that uric acid-induced CRP in endothelial cells could inhibit the release of NO, and that NO levels could be in turn normalized with the treatment of anti-CRP antibodies ( 73 ). Finally, in addition to endothelial dysfunction, fructose metabolism has also been linked to hypertension through other mechanisms, such as sympathetic hyperactivity ( 77 ), inhibition of the effect of acetylcholine and prostacyclin ( 78 , 79 ), and the enhancement of the effect of vasoconstrictor substances ( 80 ).

Apart from hypertension, fructose-induced uricemia can be linked to atherosclerosis through chronic inflammation and the enhanced production of chemokines that attract the recruitment of monocytes. Indeed, past studies have shown that lowering uric acid concentration has significantly reduced macrophage infiltration and TNFα expression ( 81 ). Mechanistically, uric acid has been shown to induce the formation of NLRP3 inflammasome ( 82 ), as well as activating p38 MAPK signaling pathways. It is well established that NLRP3 inflammasome is critical for the production of IL-1β, which has been recently shown to be involved in the development of atherosclerosis ( 83 ). On the other hand, the activation of p38 MAPK signaling is known to be pro-atherogenic due to its downstream cascades involving the production of adhesion molecules and induction of angiogenesis ( 84 ). Taken together, fructose-induced uricemia can be linked to the progression of atherosclerosis through endothelial dysfunction, enhanced inflammation, and increased recruitment of monocytes into the intima.

Fructose-induced insulin resistance

Long-term excess hepatic fructose metabolism can also induce insulin resistance ( 85 – 87 ), which is another strong predictor of atherosclerosis ( 88 – 90 ), specifically inducing hepatic insulin resistance prior to whole body insulin resistance ( 91 , 92 ). In recent years, several mechanisms have been proposed to link fructose metabolism with hepatic insulin resistance, in which most of these pathways are associated with the consequential effects of de novo lipogenesis post hepatic fructolysis. As previously described, hepatic fructose metabolism is intrinsically lipogenic ( 93 ), and that various lipotoxic intermediates and fatty acids are created during this process. Notably, several of these intermediates, such as diacylglycerol (DAG) and ceramides, have been shown to promote hepatic insulin resistance ( 94 – 96 ).

DAG is the immediate precursor of TG as Diacylglycerol O-acyltransferase catalyzes the conversion of DAG with fatty acyl CoA into TG. In the context of excess hepatic lipid synthesis, DAG was shown to activate protein kinase C (PKC)ε and inhibit hepatic insulin signaling. Specifically, DAG induces PKCε translocation to the plasma membrane and phosphorylates insulin receptor tyrosine kinase (IRTK) at Thr1160, thus inhibiting its activity and insulin-mediated phosphorylation on insulin receptor substrate 2 (IRS2) ( 97 , 98 ). More importantly, knocking down PKCε with an antisense oligonucleotide has reversed the effects of fat-induced hepatic insulin resistance in rats ( 98 ), which directly supports the DAG-PKCε induced hypothesis of hepatic insulin resistance.

Apart from DAG, ceramides have also been proposed to promote hepatic insulin resistance as well. Ceramides are sphingolipids that are produced from sphingosine and fatty acids, and generally play an important role in regulating cell membrane stabilization and distribution of signaling proteins ( 96 ). In the context of hepatic insulin resistance, it was shown that ceramide can either activate PKCζ and block protein kinase B (AKT) from participating insulin-mediated signaling, or activate protein phosphatase 2A, which is responsible for the dephosphorylation and thus inactivation of AKT ( 99 , 100 ). In addition to lipotoxic intermediates, other metabolites secreted during hepatic fructolysis, such as fructose-derived lactate, are also linked to insulin resistance. As previously described, lactate accounts for 25% of secreted metabolites post fructolysis, and elevation of plasma lactate levels is known to induce peripheral insulin resistance ( 101 ). Mechanistically, lactate infusion has suppressed the ability of insulin to stimulate IRS2-associated phosphatidylinositol 3-kinase and AKT activities in skeletal muscle cells ( 102 ). Taken together, these studies have collectively demonstrated that metabolites post fructolysis in the liver are linked to both hepatic and peripheral insulin resistance.

Fructose-induced lipoprotein hyperproduction

A significant connection between excess fructose consumption and atherosclerosis is fructose-induced lipoprotein hyperproduction in the liver, such as very low-density lipoproteins (VLDL). VLDL, a long-established marker for atherosclerosis ( 103 , 104 ), are mainly produced in the liver and their synthesis is dependent on the availability of lipid substrates, such as triglycerides, fatty acids and Apolipoprotein B100 (ApoB) ( 105 – 108 ). As previously described, excess hepatic metabolism of fructose significantly contributes to de novo lipid synthesis, hence leading to the overproduction of VLDL into the circulation, eventually raising plasma triglyceride levels. Indeed, a past study has shown that the levels of plasma ApoB, small dense LDL and oxidized LDL were increased in subjects that that have consumed fructose ( 93 ), thus strengthening the association between postprandial hypertriglyceridemia and proatherogenic conditions.

The elevation of lipid biosynthesis is a consequence of activating the expression of genes related to lipid synthesis, as well as suppressing the expression of genes related to fatty-acid oxidation. For instance, sterol regulatory element binding protein (SREBP), the master transcription factor that regulates the synthesis of cholesterol and fatty acid, one of its isoforms (SREBP-1) was found to be activated in mice post 60% fructose diet ( 109 ). Mechanistically, the binding activity of SREBP-1 was found to be regulated by insulin signaling, specifically through the MAPK signaling ( 110 ), although some studies have also shown SREBP-1 can be activated independently of insulin ( 111 , 112 ). Apart from SREBP-1, ChREBP is another well-known transcription factor that regulates the expression of lipogenic genes upon activation by carbohydrate metabolites in the liver. A past study has shown that high-fructose diet in mice activated hepatic ChREBP and expression of its targeted genes involved in lipid synthesis, a process also independent of insulin signaling ( 58 ). Finally, the inhibited expression of genes involved in fatty acid oxidation also contributes to the overproduction of lipoproteins. For example, the α member of the Peroxisome proliferator-activated receptors (PPAR) family regulates fatty acid oxidation genes, and was shown to be suppressed in fructose-fed rats ( 113 ). In addition to transcriptional regulation, past studies have also shown that fructolysis-derived methylglyoxal, as well as uric acid inhibiting adiponectin secretion from adipocytes, contributed to the inhibition of AMPK signaling, which further inhibits fatty acid oxidation in hepatocytes. Taken together, these studies have demonstrated that both transcriptional and post-transcriptional mechanisms are involved in regulating the enhancement of hepatic lipid synthesis post excess fructose feeding.

Fructose-induced chronic inflammation

The success of the CANTOS trial, which targets interlukin-1β with a therapeutic monoclonal antibody, supports the notion that reducing inflammation independent of lowering lipid levels can lower the risk of cardiovascular diseases. The result of the trial also implicates that targeting other inflammatory pathways, such as the ones mediated by interlukin-6 or tumor necrosis factor alpha (TNFα) ( 83 ), can display therapeutic benefits, yet the molecular mechanisms behind how inflammation is initiated in atherosclerosis remains controversial ( 1 ). For instance, past studies have shown that the accumulation of oxLDL or cholesterol crystals in macrophages is an intrinsically inflammatory process ( 114 , 115 ). However, other studies have shown that oxLDL or cholesterol accumulation in macrophages significantly suppressed their inflammatory responses due to rewiring of their metabolism ( 116 – 119 ). More recently, transcriptomic analyses of mouse and human atherosclerotic lesions have revealed that foamy macrophages are in fact less inflammatory than non-foamy macrophages ( 120 , 121 ). Taken together, these studies support the notion that while hypercholesterolemia is a key risk factor of atherosclerosis, oxLDL or cholesterol do not intrinsically activate inflammation in Mφs both in vitro and in vivo .

On the contrary, there is an increasing appreciation in the immunometabolism field that fructose metabolism and macrophage inflammatory responses are connected. For instance, it has been shown that intrinsic fructose metabolism of macrophages can promote their inflammatory responses in a glutamine-dependent oxidative metabolism manner ( 122 ). In addition to its intrinsic inflammatory effects, excess fructose metabolism has been recently demonstrated to cause endotoxemia and promote systemic inflammation ( 13 ). Mechanistically, this study has shown that excess fructose metabolism could trigger intestinal barrier deterioration and promote the leakage of microbial-derived products into the portal circulation. These products could subsequently activate residential macrophages in the liver (Kupffer cells), stimulate their production of TNF-α in a Toll-like receptor 4-dependent manner, in which the secreted TNF-α could then stimulate de novo lipogenesis in hepatocytes ( 13 ). Similar findings were also reported by Kavanagh et al. where the authors found that a high fructose diet induced gut microbial translocation, endotoxemia and liver damage in non-human primates ( 123 ). Collectively, these studies have demonstrated that high fructose diets can intrinsically and extrinsically promote macrophage inflammatory responses through metabolic means.

Apart from macrophages, fructose has also been linked to inflammation through the formation of advanced glycation end (AGEs) products and its subsequent activation of its receptor, known as receptor for advanced glycation end product (RAGE). Interestingly, this effect has been shown in multiple cell types, such as dendritic cells ( 124 ) and endothelial cells ( 125 ), which implies that the underlying molecular mechanism is conserved, and its downstream inflammatory effect is likely systemic. In general, the formation of AGE by fructose involves the interaction between the reactive carbonyls in fructose and the amino groups of proteins, DNA and lipids, known as the Millard reaction. Unlike glucose, fructose is significantly more reactive in Maillard reaction due to its keto group and its enhanced stability in its open chain formation ( 126 – 130 ). Post Maillard reaction, fructose generates early glycation products which undergo a series of further conversion into AGEs ( 131 ). In hepatocytes, where fructose metabolism largely takes place, the formation of AGEs has been detected by Liquid chromatography–mass spectrometry (LC–MS) in fructose-drinking mice, and the by-products of AGEs formation, such as N-carboxymethyllysine, are linked to the enhancement of de novo lipid synthesis in hepatocytes ( 132 ).

Apart from enhancing lipid synthesis, the classical consequence of AGEs is the binding of RAGE and the initiation of downstream inflammatory signaling cascades. For instance, exposing dendritic cells with fructose (15 mM) has led to the increased formation of AGEs, and its binding to RAGE caused an increased production of inflammatory cytokines, such as IL-1β and IL-6, in a NF-κB-dependent manner ( 124 ). This increased inflammatory response is accompanied by a shift of metabolism from oxidative metabolism to glycolysis. Similarly, exposing endothelial cells with fructose has induced AGEs formation, and subsequently led to the formation of oxidative stress and inflammatory reactions also in a NF-κB-dependent manner ( 133 , 134 ). Overall, these studies collectively support the notion that fructose-induced AGEs formation is likely a systemic inflammatory process that occurs in both immune and non-immune cell types.

Conclusion and future perspectives

Within the past decades, our consumption of dietary fat has significantly declined ( 23 , 24 ) yet the incidence of cardiovascular diseases has continued to rise, thus implicating the possibility that dietary fat may not be the culprit behind the prevailing cardiovascular mortalities. On the other hand, dietary fructose consumption has tremendously increased and positively correlated with the rise of metabolic diseases, including but not limited to diabetes, obesity and atherosclerosis ( 5 , 6 , 8 ). In the 1970s, John Yudkin first proposed the notion that sugar is the dietary factor that contributes to cardiovascular illnesses ( 7 , 17 , 20 ). Although his hypothesis was initially dismissed, researchers have been revisiting his hypothesis for the last 25 years and have accumulated strong and convincing evidence to support it. To date, our understanding of fructose metabolism, specifically in the intestine and liver, has significantly advanced. However, the mechanistic link between excess fructose metabolism and the development of cardiovascular diseases, such as atherosclerosis, is only beginning to be appreciated. Furthermore, how excess fructose metabolism contributes to the induction of inflammation in atherosclerosis remains to be determined. In this review, I have provided an overview of the metabolic consequences after excess fructose metabolism and how these consequences are potentially linked to atherosclerosis as risk factors ( Figure 3 ).

Figure 3 . Excess dietary fructose metabolism and risk factors of atherosclerosis. Diagram that summarizes the various metabolic consequences of excess dietary fructose metabolism and its association with atherosclerosis as risk factors. This includes uricemia-induced hypertension, lipotoxic intermediates-mediated insulin resistance, lipoprotein overproduction and chronic inflammation. Created with Biorender.com .

Upon reviewing the current literature on fructose-related studies, several important considerations need to be taken during the design of mechanistical studies to ensure the results are not confounded by third-variables, one of which is the administration of fructose. In general, for in vivo studies, many investigators typically feed rodents with fructose-rich drinks or diet ad libitum . However, this may pose a problem as fructose is intrinsically addictive ( 135 ) and fructose-fed rodents may consume more calories than the control diets. Therefore, isocaloric diets, such as administered through oral gavage, is always a better approach as it prevents excess caloric intake from confounding the experimental results. Apart from this, the type of fructose diet, such as purified fructose or in the form of sucrose, should also be considered. Since humans do not typically consume fructose in its purified form, and past studies have already shown that the uptake of fructose is enhanced in the presence of glucose ( 44 ), this suggests that a sucrose diet should be favored over a purified fructose diet to ensure physiological relevance. Since the sucrase-isomaltase expressed in the intestine can also cleave maltose, in addition to sucrose, a maltose diet can be used as a control diet when compared against the experimental sucrose diet. Interestingly, a recent study has also shown that sucrose-fed mice with drinking water versus solid diet yielded differential metabolic results despite consuming equivalent amounts of sucrose ( 136 ). In humans, it has been suggested that liquid sugars are more detrimental with regards to body weight than its solid forms, most likely due to the fact that sugar beverages produce less satiety and thus leads to decreased dietary energy compensation and increased intake ( 137 – 139 ). However, only one clinical dietary intervention study has been conducted comparing the effects of sustained consumption of liquid versus solid sugar, and a significant group difference in body weight was not observed ( 138 ). Importantly, a recent prospective study found that both added sugar in food and added sugar in beverage increased the risk of developing metabolic syndrome during a 30-year follow-up ( 140 ). Therefore, more studies comparing liquid versus solid sugar are needed to determine whether the consumption of liquid sugar is more detrimental than solid sugar and investigators need to be aware of this. Finally, investigators should also consider the type of cells they are investigating in relation to fructose metabolism. In this review, some in vitro studies have incubated their cells of interest with fructose at a concentration that is unlikely to be physiologically relevant. On the other hand, investigators should also be aware that certain organs, despite not being directly related to fructose metabolism, do endogenously synthesize fructose post hyperglycemic conditions, such as the brain ( 141 ). This suggests that the effects of dietary fructose consumption can extend to other cell types beyond their metabolizing organs.

In 1972, Yudkin wrote in his book, Pure, White, and Deadly: How Sugar Is Killing Us and What We Can Do to Stop It, “if only a small fraction of what is already known about the effects of sugar were to be revealed in relation to any other material used as a food additive, that material would promptly be banned” ( 18 ). While this message was ignored for 25 years, the book was republished in 2012 with a forward by Dr. Robert Lustig. Although our current understanding of how excess fructose metabolism is causally linked to cardiovascular mortality is still rudimentary, its detrimental metabolic consequences have already been revealed and continuously supported by ongoing research. Continued research is warranted to further elucidate the mechanistic link between fructose metabolism and cardiovascular disease.

Author contributions

KT: Writing – original draft, Writing – review & editing.

The author declares that financial support was received for the research, authorship, and/or publication of this article. KT was supported by fellowships from Canadian Institutes of Health Research, Ontario Graduate Scholarship, fellowships from the University of Toronto and The Peterborough K. M. Hunter Charitable Foundation.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: atherosclerosis, John Yudkin, inflammation, sucrose, sugar, uricemia, lipoprotein hyperproduction, fructose

Citation: Ting KKY (2024) John Yudkin’s hypothesis: sugar is a major dietary culprit in the development of cardiovascular disease. Front. Nutr . 11:1407108. doi: 10.3389/fnut.2024.1407108

Received: 26 March 2024; Accepted: 24 June 2024; Published: 04 July 2024.

Reviewed by:

Copyright © 2024 Ting. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Kenneth K.Y. Ting, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Young Sheldon’s Secret Big Bang Theory Cameo Highlighted The Original Show’s Best Relationship

Ncis' nick torres changes have set up the perfect story for season 22, game of thrones: 10 strongest castles in westeros, ranked.

Young Sheldon 's series finale potentially killed off Leonard from The Big Bang Theory , making one of the original show's plotlines even more polarizing.
Leonard's death would have a significant impact on his and Penny's children and reshape any potential sequels to The Big Bang Theory .
The tragic twist could be resolved in future spinoffs, as it is highly unlikely that The Big Bang Theory would kill off its main character in a throwaway line from another series.

The fact that Young Sheldon ’s series finale may have killed off The Big Bang Theory ’s Leonard makes the original show’s most divisive plot line even worse. The Big Bang Theory ’s finale may not have been perfect, but it didn’t set the Internet ablaze with fan theories about a potential cryptic death announcement. The same can’t be said for Young Sheldon ’s ending. In its final episode, Young Sheldon featured a moment where Sheldon mentioned that The Big Bang Theory ’s heroine Penny babysat his and Amy’s children. The fact that he didn’t mention Leonard set off some alarm bells.

Leonard was central to The Big Bang Theory ’s cast of characters , playing as big a role in the original show as Sheldon himself. Johnny Galecki’s sweet nerd was the show’s primary protagonist for the first few seasons until Sheldon proved incredibly popular and became its breakout character. As such, Leonard’s offscreen death would completely reshape any potential sequels to The Big Bang Theor y. It would also have a massive impact on a storyline that was already divisive among viewers and critics. The most infamous part of The Big Bang Theory ’s series finale would be even worse after Leonard’s death.

Iain Armitage's Sheldon lies on a bed looking worried in Young Sheldon season 3

Young Sheldon hid a secret uncredited cameo from one of The Big Bang Theory's most famous stars, thus highlighting the importance of their TBBT role.

Young Sheldon’s Potential Leonard Death Puts A Tragic Spin On TBBT’s Pregnancy Ending

Penny and leonard’s children wouldn’t be too old by young sheldon’s ending.

Leonard’s death would add a tragic shadow to Penny’s already controversial finale pregnancy since it would mean that the show’s lead couple never got to raise their children together. After all, Sheldon and Amy aren’t much older in Young Sheldon' s finale, and, judging by their activities, their children are still young in the episode. It is reasonable to presume that Leonard and Penny’s child would also still be relatively young and, since it is not clear when Leonard died, it is tragically likely that he would have missed out on being a father despite his desire to experience this.

Whenever Leonard and Penny discussed the possibility of having children, Leonard was always significantly more invested in the idea than his partner. Leonard and Penny had numerous major fights over this, and this made The Big Bang Theory ’s series finale twist controversial. Penny spent eleven seasons repeatedly stating that she had no interest in becoming a parent, even weathering insults from her friends Bernadette and Amy. It was only in season 12 that she showed any uncertainty around this resolve, and even these comments were still only briefly referenced before the show’s finale suddenly revealed her unexpected pregnancy.

Penny's pregnancy came as a shock rather than a foreshadowed plot development.

Why The Big Bang Theory Finale’s Pregnancy Was Already Divisive

Penny’s change of mind wasn’t universally well-received.

Numerous critics and fans online noted that Penny was adamant about not wanting children until the final season and, as such, her pregnancy came as a shock rather than a foreshadowed plot development. As recently as the season 12 premiere, Penny reminded Leonard that she didn’t want children after a pregnancy scare . This wasn’t the first time that Kaley Cuoco’s The Big Bang Theory heroine pulled the short straw in terms of life plans, either. The Big Bang Theory ’s worst crime against Penny arrived long before her pregnancy when she had to drop her dreams of becoming an actor.

Penny repeatedly reaffirmed that she didn’t want to get pregnant throughout season 12

In a series where Howard went to space and Sheldon won a Nobel Prize, Penny’s comparatively modest hopes of becoming an actor were too unrealistic and idealistic for her to succeed. As such, the show had already limited her agency in comparison to other characters before season 12 began. The fact that Penny repeatedly reaffirmed that she didn’t want to get pregnant throughout season 12 made this twist more controversial, especially after an episode where Leonard considered sperm donation. The finale suddenly revealing she was pregnant was the conclusive proof that her dreams and ambitions weren’t the show's central focus.

Young Sheldon’s Finale May Not Have Killed Off Leonard

Sheldon’s reference to penny doesn’t confirm leonard’s death.

Johnny Galecki as Leonard Hofstadter in The Big Bang Theory smiling in front of a yellow and red background

Fortunately, viewers may not need to worry about this downbeat twist. Young Sheldon ’s finale may not have killed off Leonard since there are plenty of other reasons that Sheldon wouldn’t have mentioned Leonard. Leonard may not have been present while Penny was babysitting Amy and Sheldon’s children as he may have been working or otherwise detained. Young Sheldon ’s potential Leonard death could also be retconned if Leonard was present for this babysitting, but Sheldon didn’t mention him since he didn’t feel he was responsible for its impact on his daughter. Context is crucial in the scene’s phrasing.

Sheldon says that he should never have allowed Penny to babysit upon discovering that his daughter wants to pursue acting. Since this was always Penny’s dream, it is fair for Sheldon to presume that Penny was the one who put the idea in his daughter’s head. Even if Leonard was present, his lack of interest and experience in the world of acting means he would be less likely to mention it to Sheldon and Amy's daughter. Thus, Leonard’s apparent death might be nothing more than a case of miscommunication on Sheldon’s part that has been taken out of context.

It is highly unlikely that The Big Bang Theory would kill off its hero in a throwaway line from another series.

Future The Big Bang Theory Spinoffs Can Resolve Leonard’s Fate

Leonard’s fate could be explained in the big bang theory’s sequel.

Although The Big Bang Theory had a few deaths , these were mostly comical fates that befell unseen side characters. It is highly unlikely that The Big Bang Theory would kill off its hero in a throwaway line from another series, and its subsequent spinoffs can clarify this. Admittedly, Georgie and Mandy’s First Marriage is unlikely to mention the issue since the series will focus on characters established in Young Sheldon and will take place before The Big Bang Theory . However, creator Chuck Lorre said in 2023 that he was working on another series set in the sitcom's fictional universe.

This spinoff from The Big Bang Theory could clarify whether Leonard survived, with the show most likely confirming that he lived. Any follow-up to the original series would need Leonard as a major player , even if it was a sequel series that focused mostly on the next generation of characters like That ‘70s Show 's spinoff That ‘90s Show . Thus, it is likely that the next spinoff from The Big Bang Theory will prove that Penny and Leonard’s finale fate wasn’t as tragic as Young Sheldon ’s series finale made it seem, despite the theories that spawned from Sheldon’s mention of Penny.

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How to Write a Strong Hypothesis
Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.
2.4 Developing a Hypothesis
Theories and Hypotheses. Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena.Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions ...
Hypothesis: Definition, Examples, and Types
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
What is a Hypothesis
Examples of Hypothesis. Here are a few examples of hypotheses in different fields: Psychology: "Increased exposure to violent video games leads to increased aggressive behavior in adolescents.". Biology: "Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.".
Developing a Hypothesis
Theories and Hypotheses. Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes ...
Research: Articulating Questions, Generating Hypotheses, and Choosing
The hypothesis is a tentative prediction of the nature and direction of relationships between sets of data, phrased as a declarative statement. Therefore, hypotheses are really only required for studies that address relational or causal research questions.
Research Hypothesis: Definition, Types, Examples and Quick Tips
3. Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.
Research Hypothesis In Psychology: Types, & Examples
A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
Research Hypothesis: What It Is, Types + How to Develop?
A research hypothesis helps test theories. A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior. It serves as a great platform for investigation activities.
How to Write a Strong Hypothesis
Hypotheses propose a relationship between two or more variables. An independent variable is something the researcher changes or controls. ... Developing a hypothesis (with example) Step 1: Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable ...
What is a Research Hypothesis and How to Write a Hypothesis
The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem. 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a 'if-then' structure. 3.
Developing a Hypothesis
Theories and Hypotheses. Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes ...
Hypothesis Testing
It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories. There are 5 main steps in hypothesis testing: State your research hypothesis as a null hypothesis and alternate hypothesis (H o) and (H a or H 1). Collect data in a way designed to test the hypothesis. Perform an appropriate ...
A Strong Hypothesis
Make sure your hypothesis is a specific statement relating to a single experiment. Putting it in Action To help demonstrate the above principles and techniques for developing and writing solid, specific, and testable hypotheses, Sandra and Kristin, two of our staff scientists, offer the following good and bad examples.
Chapter 6
A hypothesis is often defined as an educated guess because it is informed by what you already know about a topic. This step in the process is to identify all hypotheses that merit detailed examination, keeping in mind that there is a distinction between the hypothesis generation and hypothesis evaluation.. If the analysis does not begin with the correct hypothesis, it is unlikely to get the ...
PDF DEVELOPING HYPOTHESIS AND RESEARCH QUESTIONS
DEVELOPING HYPOTHESES & RESEARCH QUESTIONS Definitions of hypothesis "Hypotheses are single tentative guesses, good hunches - assumed for use in devising theory or planning experiments intended to be given a direct experimental test when possible". (Eric Rogers, 1966)
How to Write a Hypothesis? Types and Examples
A research hypothesis can be defined as a specific, testable prediction about the anticipated results of a study. 2 Hypotheses help guide the research process and supplement the aim of the study. After several rounds of testing, hypotheses can help develop scientific theories. 3 Hypotheses are often written as if-then statements.
How To Develop a Hypothesis (With Elements, Types and Examples)
4. Formulate your hypothesis. After collecting background information and making a prediction based on your question, plan a statement that lays out your variables, subjects and predicted outcome. Whether you write it as an "if/then" or declarative statement, your hypothesis should include the prediction to be tested.
Literature Review and Hypotheses
admin September 9, 2016 Blog, Literature Review and Hypotheses. A literature review shows the cumulative knowledge which is the conceptual framework your study is based. It gives an overview of prior research identifying the details of the need for your study stated in your introduction section. It is common to present the literature with ...
A Beginner's Guide to Hypothesis Testing in Business
3. One-Sided vs. Two-Sided Testing. When it's time to test your hypothesis, it's important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests, or one-tailed and two-tailed tests, respectively. Typically, you'd leverage a one-sided test when you have a strong conviction ...
PDF Hypothesis Development and Testing
It could be considered as the working instrument of theory. Hypotheses can be deduced from theory and from other hypotheses. It could be tested and shown to be probably supported or not supported, apart from man's own values and opinions. 2.2 Characteristics of a Good Hypothesis A good hypothesis must be based on a good research question.
What is Hypothesis
Hypothesis. Last Updated : 04 Jun, 2024. Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.
Contrasting philosophical and scientific views in the long history of
The first chromosome theory of heredity and development by Theodor Boveri and Sutton was confirmed and further developed by Thomas H. Morgan and his school of Drosophila genetics in the United States in the 1910s. In general, geneticists were more inclined to bridge the gap between the two fields of heredity and development.
RFK Jr. dredges up conspiracy theory haunting campaign: 'As President I
Robert F. Kennedy Jr., independent presidential candidate, shared on X that he "won't take sides on 9/11," adding to his slew of entertaining conspiracy theories.
Which Description Relates To Gesells Theory Of Development?
Gesell's theory of development is based on the concept of maturation, where development occurs in a predictable and sequential pattern.. Gesell believed that each child has a unique developmental timetable, and that they progress through a series of stages that are determined by their biological and genetic makeup.Gesell's theory emphasizes the role of nature over nurture in development, and ...
John Yudkin's hypothesis: sugar is a major dietary culprit in the
The intrinsic metabolism of excessive fructose in the liver, but not the intestine, eventually leads to the development of many features of metabolic syndrome, such as hepatic steatosis and hypertension , which are also risk factors for developing atherosclerosis. Therefore, in this review, I will review John Yudkin's hypothesis, with the ...
Corey LaJoie may have cracked the code with his theory on Kyle Busch
Corey LaJoie has a theory for why Busch and the No. 8 team simply don't have it this year and spoiler, it has to do with the simulator. Corey LaJoie unveils theory for Kyle Busch's struggles "There's been a lot of chatter about the 8 not running near what we expect from Kyle Busch, a two-time champion, to run," LaJoie said this week ...
The game theory that led to nuclear standoffs
Last week, Vladimir Putin vowed to make new nuclear weapons and consider placing them close to NATO countries. Meanwhile, here in the US, the government boosted its nuclear weapon spending by 18% ...
CDMRP Neurofibromatosis Exploration-Hypothesis Development Award
University of Houston Cullen College of Engineering Engineering Building 2, Room E421 4222 Martin Luther King Boulevard Houston, TX 77204-4007. Contact Us
Leonard's Young Sheldon Death Would Make The Big Bang Theory's Most
The fact that Young Sheldon's series finale may have killed off The Big Bang Theory's Leonard makes the original show's most divisive plot line even worse. The Big Bang Theory's finale may not have been perfect, but it didn't set the Internet ablaze with fan theories about a potential cryptic death announcement. The same can't be said for Young Sheldon's ending.

2.4 Developing a Hypothesis

Theories and Hypotheses

Theory Testing

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

Key Takeaways

Share This Book

How to Write a Great Hypothesis

Hypothesis Format

Hypothesis Types

At a Glance

The Hypothesis in the Scientific Method

Elements of a Good Hypothesis

How to Formulate a Good Hypothesis

The Importance of Operational Definitions

Replicability

Hypothesis Checklist

A few examples of simple hypotheses:

Examples of a complex hypothesis include:

Examples of a null hypothesis include:

Examples of an alternative hypothesis:

Collecting Data on Your Hypothesis

Descriptive Research Methods

Experimental Research Methods

What is a Hypothesis – Types, Examples and Writing Guide

Types of Hypothesis

Research Hypothesis

Null Hypothesis

Alternative Hypothesis

Directional Hypothesis

Non-directional Hypothesis

Statistical Hypothesis

Composite Hypothesis

Empirical Hypothesis

Simple Hypothesis

Complex Hypothesis

Applications of Hypothesis

How to write a Hypothesis

Identify the Research Question

Conduct a Literature Review

Determine the Variables

Formulate the Hypothesis

Write the Null Hypothesis

Refine the Hypothesis

Examples of Hypothesis

Purpose of Hypothesis

When to use Hypothesis

Characteristics of Hypothesis

Advantages of Hypothesis

Limitations of Hypothesis

About the author

Muhammad Hassan

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Developing a Hypothesis

Learning Objectives

Theories and Hypotheses

Theory Testing

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

Share This Book

Research: Articulating Questions, Generating Hypotheses, and Choosing Study Designs

TURNING CURIOSITY INTO QUESTIONS

TYPES OF RESEARCH QUESTIONS

Descriptive

DECIDING WHETHER TO ANSWER A RESEARCH QUESTION

GENERATING AIMS AND HYPOTHESES

CHOOSING THE STUDY DESIGN

CONCLUSIONS

Further Reading

The Craft of Writing a Strong Hypothesis

Table of Contents

What is a Hypothesis?

Different Types of Hypotheses‌

1. Null hypothesis