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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."

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Hypothesis Examples

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method . A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H 0 ) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable ( independent variable ) will have no effect on the variable being measured ( dependent variable ). Here are null hypothesis examples:

• Plant growth is unaffected by temperature.
• If you increase temperature, then solubility of salt will increase.
• Incidence of skin cancer is unrelated to ultraviolet light exposure.
• All brands of light bulb last equally long.
• Cats have no preference for the color of cat food.
• All daisies have the same number of petals.

Sometimes the null hypothesis shows there is a suspected correlation between two variables. For example, if you think plant growth is affected by temperature, you state the null hypothesis: “Plant growth is not affected by temperature.” Why do you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier applying a statistical test that shows, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H 1 ) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. In other cases, the hypothesis shows a correlation between two variables. Here are some research hypothesis examples:

• If you leave the lights on, then it takes longer for people to fall asleep.
• If you refrigerate apples, they last longer before going bad.
• If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower).
• If you leave a bucket of water uncovered, then it evaporates more quickly.
• Goldfish lose their color if they are not exposed to light.
• Workers who take vacations are more productive than those who never take time off.

Is It Okay to Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test yourself with a Scientific Method Quiz .

• Mellenbergh, G.J. (2008). Chapter 8: Research designs: Testing of research hypotheses. In H.J. Adèr & G.J. Mellenbergh (eds.), Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing.
• Popper, Karl R. (1959). The Logic of Scientific Discovery . Hutchinson & Co. ISBN 3-1614-8410-X.
• Schick, Theodore; Vaughn, Lewis (2002). How to think about weird things: critical thinking for a New Age . Boston: McGraw-Hill Higher Education. ISBN 0-7674-2048-9.
• Tobi, Hilde; Kampen, Jarl K. (2018). “Research design: the methodology for interdisciplinary research framework”. Quality & Quantity . 52 (3): 1209–1225. doi: 10.1007/s11135-017-0513-8

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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

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 .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, 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 types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

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 ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize 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.

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

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.

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 .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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See an example

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.

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.

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Types of Hypothesis

‘Hypothesis’ is a word I see being thrown around social media; sometimes it just doesn’t sit right. This inspired me to look into various technical resources to see if any types of hypothesis actually fit the use I saw so many insisting was “correct”. This article hopes to address the use of hypothesis in the colloquial setting, give more detail around the common misconception, and provide details about the various types of hypothesis.

Obviously, language is one of those things that is used to convey meaning, so any word can be used to mean anything and if enough people understand you, it is a successful use of the language .

The problem with the way hypothesis is being used is that it is a technical term, primarily used in science and statistics, so it has a contextually correct definition/set of parameters. What compounds the issue is, that most who are using the term think they are doing it in a technical way. This might lead them to assume there is a null hypothesis to the “hypothesis” they are discussing.

The Common Misconception

What is a variable, example: the lawn hypothesis, hypothesis – the required components, prediction vs hypothesis, causal hypothetical process, examples of causal hypothesis, non-directional hypothesis, directional hypothesis, relational hypothesis / correlative hypothesis / associative hypothesis, the descriptive hypothesis/tentative law., statistical hypothesis, null and alternative hypothesis., simply complex, quick recap on the types of hypothesis, explanation, predictive qualities..

• Operationalisation & Falsification

What would we call a Non-Falsifiable Hypothesis?

The most common misconception I see is that “a hypothesis is just an idea or explanation about the natural world” and you can forgive this misconception because if you only do a search for a definition and stop there, you don’t get much more information than that.

As is often the case with these simplistic definitions of technical language, they miss the actual context and application of these terms. You can see here that one of the synonyms is conjecture, an opinion formed on incomplete information. But that doesn’t really describe what a hypothesis is, and how it is used/applied.

What is a Hypothesis?

A hypothesis is an explanation of an observation of some feature of the natural world or dataset that suggests a relationship between two (or more) variables. The hypothesis, whilst not a prediction itself, has a predictive quality that, if the hypothesis is supported, we can expect to see.

A variable is an element, feature, or factor that has the ability to change.

How does a variable fit in the hypothesis framework?

It might not be clear what is meant by a relationship between variables. It basically means that if we manipulate one variable we would expect the other to change. It might be better to give an example:

Let’s imagine for a moment we are in our garden and you notice that half of your grass is much longer than the other half. You note that the shorter half is in the shade. You take some time and notice that even through the day, the shorter half of your lawn is mostly in the shade, whilst the other side gets full light for a large portion of the day.

Your observation leads to the idea that the amount of sunlight directly affects the rate of growth of your grass.

This idea is an explanation, is it a hypothesis?

Well, we have our subject – the grass, and we have two variables, the amount of sunlight and the rate of growth. If we didn’t have these variables, or they were poorly defined like “stuff makes grass change” then at best we have conjecture. Therefore, if it is just conjecture, it is not really a hypothesis.

You could, however, regard conjecture as a “bad” or “baseless” hypothesis but I would suggest that this might be tied to valuing the use of technical/scientific language and forgetting the whole purpose of it.

Even in the simplistic definition at the start, it is mentioned that it is a “starting point of an investigation” – if you don’t have variables, or they are so vague you cannot identify or manipulate them, then how can you investigate?

With the lawn hypothesis, we also note its predictive quality. If the hypothesis is to be supported then we would expect to see the amount of sunlight affecting the growth.

We probably would have made our hypothesis more specific, more sunlight increases the rate of growth. So if this is supported then we would expect an increase in the amount of sunlight to increase the rate of growth.

Once we have these variables and predictions, the result of the prediction not coming true means our hypothesis has been falsified – it might be that we refine the hypothesis and set of predictions or that the hypothesis is outright proven false.

So, whilst we may not actually test our hypothesis, the hypothesis has the possibility of being falsified.

Falsification is considered a big part of a valid/credible scientific hypothesis. It is largely agreed that for a hypothesis to be considered valid/credible or even scientific, it must be falsifiable, at least in principle, especially when discussing a causal hypothesis (like sunlight increasing growth rate).

With these components, it is then possible to operationalisation the hypothesis. Essentially, design the test, the variables and so on. Operationalisation helps you turn some fuzzy or abstract concepts into more meaningful data.

• An explanation for an observation
• Variables (at least 2)
• Ability to Manipulate the Variables
• Predictive quality
• Falsifiability
• Operationalisation

Strictly speaking, these are the components that make a hypothesis valid/credible and therefore worthy of investigation.

If the only component is the explanation then it shouldn’t really be regarded as a hypothesis. At best, it’s a disregarded/weak hypothesis, but really it is just conjecture, an idea, a proposition, or a prediction.

I will come to justify this further in a future article but I do not want to dissuade and divert further from the primary focus of this article which is the ‘Types of Hypothesis’.

A prediction is an expected result if a hypothesis or theory is true. Whilst a Hypothesis ought to have a predictive quality, a prediction does not qualify as a hypothesis in and of itself. The prediction is something we draw from a hypothesis or theory.

Now that we have a clear understanding of what is meant by a hypothesis, and how it ought to be used, let us now take a look at the different types of hypotheses.

There’s overlap between types of hypothesis and they can be described slightly differently in different sciences, though largely they are described the same way, and the key features are still present.

Causal hypothesis

The causal hypothesis is generally the “main” hypothesis spoken about within science. In fact, it is very rare to hear ‘hypothesis’ being used in any other way in the scientific domain, and this is mostly due to what science is used for; investigating the causal links between natural phenomena.

e.g Something is observed, an explanation is proposed and a causal link is looked for. By manipulating an independent variable, we expect that to cause a change within a dependent variable.

• Observe some phenomenon
• Ask what caused this
• Advancing a causal hypothesis (defined as a proposed explanation) for what has been observed (e.g., “the grass grows better on this side because it is exposed to more sunlight on this side”).
• Planning a test of the hypothesis that incorporates the generation of a prediction from the hypothesis.
• Conducting the test and comparing the results with the prediction.
• Drawing a conclusion as to whether the results of the test support or contradict the hypothesis.

Assuming we’ve observed some phenomena:

If we increase the amount of caffeine taken we will increase the level of activity in a specific group in comparison to a group which has less/no caffeine.

If we increase the amount of vegetables a group eats then their weight loss will be improved due to having less room for snacking and better digestive operation.

The Lawn Hypothesis is also an example of a causal hypothesis.

The causal hypothesis could be split into 3 hypotheses, non-directional, positive direction, and negative direction.

A non-directional hypothesis does not state the change or relationship between the variables. A non-directional hypothesis is proposed when you’re not sure the effect is going be from manipulating the variables.

Caffeine causes a change in activity level/heart rate. Eating more vegetables will change how much weight you gain or lose.

On the flip side, the directional hypothesis does have a direction.

Positive: Caffeine causes and increase in activity level/heart rate. Eating more vegetables will increase your weight loss.

Negative: Caffeine causes a decrease in the activity level/heart rate. Eating more vegetables will increase your weight loss.

A relational hypothesis looks at the degree of overlap between the variables. So, instead of looking for a causal link and comparing it against a control group, the relational hypothesis simply looks for correlative qualities, and the more the variables seem to overlap, the stronger the relationship is said to be.

The variables are interdependent, that is, manipulating (or seeing a change in) one affects the other and vice versa, and the closer the associated link the better the hypothesis. Consider things like the amount of funding a hospital gets vs wait times or mortality rates. Religious upbringing and having a good education and so on.

Of course, correlation is not causation. Whilst it can be indicative of a link, sometimes you end up with this sort of thing:

Even though the Nick Cage correlation seems ridiculous, we could still test this by not letting Cage be in any new roles for a few years.

In this time he could be recording many films and then release them all in the same year once he had at least double the current max, we would then have an example of extremes to see if the correlation still applied and if it didn’t, whilst not necessarily falsified it would show that the relationship is weaker than the graph makes it look. Enough examples of weakness could lead to the hypothesis being rejected.

Of course, getting Nick to agree to this test is a wholly different ball game.

The descriptive hypothesis, better described as the tentative law, is an observation of a regular occurrence or pattern. Essentially, it’s a statement based on induction… E.g. all swans are white.

The descriptive hypothesis is better not considered a “real hypothesis” in science, because whilst it is falsifiable in theory, it’s not necessarily falsifiable in principle e.g. the black swan observation is only really “testable” by observing all species around the world, at least until you find a non-white swan, which isn’t really practical or even possible for an individual to do this in an instance.

Descriptive hypotheses can be built into law which can feed into causal hypotheses and then produce those proper predictive qualities.

There are still variables, e.g. in the example of the swan it’s the colour of the swan and the amount of the world that has been observed. Whilst the variables are defined and quantifiable, they are not in the same league as the sort of variables for a causal hypothesis.

The statistical hypothesis is the hypothesis from which the null hypothesis was born.

The statistical hypothesis is a statement about the nature of a population or dataset. It’s often stated in terms of a population parameter.

Statistical hypotheses are probabilistic and made falsifiable by setting up rejection parameters.

Like the causal hypothesis, it works in relation to a dependent and independent variable. These are set up in the form of null and alternative hypothesis.

The alternative hypothesis (or hypotheses) Ha/h1-n proposes a significance in the relationship between the variables whilst the null hypothesis suggests there’s no significant relationship between the variables.

If we consider our coffee example, the non-directional, positive directional, and negative directional are all alternative hypotheses as they all describe a relationship by manipulating the independent variable (quantity of caffeine) it causes and effect to the dependent variable, heart rate/level of activity.

The null, on the other hand, would be based on if there is no relationship between variables.. e.g. no matter how much caffeine someone had, there’s no significant difference in the heart rate/level of activity.

Hypotheses can also be split into groups like simple and complex.

A simple hypothesis is one with a single dependent and a single independent variable.

A complete Hypothesis is one with two or more dependent variables and two or more independent variables.

To lean into the coffee again, the amount of caffeine and the quantity of sugar causes and increase in the heart rate and level of perspiration.

The types of hypothesis overlap and are sometimes subsets.

E.g. the directional (or non-directional) hypotheses relate to the causal hypothesis and what sort of prediction we expect.

Simple and complex relate to the quantity of variables in a hypotheses.

The null and alternative hypothesis relate to the relationship between variables, e.g. the directional and non-directional hypotheses are alternative hypotheses and the null is when there is no significant relationship between the variables.

The statistical hypothesis doesn’t really need to be mentioned in the realm of science as it’s pretty much taken care of within the causal hypothesis.

The associative hypothesis is looking for an overlap between two variables

Essentially, if we consider all of the above types of hypothesis, they are all different sides of the same die and fit within a paradigm of alternative and null hypothesis.

That said, in science, when someone mentions a hypothesis it is almost guaranteed they are speaking of a causal hypothesis.

Fundamental qualities of a hypothesis

• Operationalisation/testability
• Falsification

A hypothesis is supposed to provide an explanation for an observation of some natural phenomenon.

All of the types of hypothesis described propose some relationship between variables. A hypothesis requires at least one independent variable and one dependent variable (or 2 interdependent variables).

The variables are quantifiable and measurable. Without these variables there can be no null hypothesis, as the null hypothesis is not merely a negation of the alternative hypothesis but the result of there being no signification relationship between the variables.

Whilst a hypothesis isn’t strictly a prediction in and of itself, through explanation and operationalisation there should be clear predictions. If the hypothesis is true then we would expect to see this result.

Predictions can extend past hypothesis testing and be additional expectations one has if a hypothesis or theory is true. Consider something like the prediction of gravitational waves long before we ever had the ability to detect them.

Operationalisation & Falsification

“A fundamental requirement of a hypothesis is that is can be tested against reality, and can then be supported or rejected.” Research Hypothesis: Definition, Types, & Examples – Simply Psychology

When we operationalise a hypothesis we are essentially designing the experiment we could perform, variables and all, and this would include our acceptance/rejection criteria. Essentially if can operationalise a hypothesis, we ought to be able to falsify it too. It isn’t really considered operationalised if part of the experiment includes technology that hasn’t been invented yet.

The doctrine of falsification states that a scientific hypothesis is only considered credible if it is falsifiable, and that means having the ability to be tested and proven wrong.

This doctrine is considered a core part of the scientific method, especially in relation to the statistical hypothesis. If I proposed a green monkey that gives aids to all those who don’t pray to it, even though it was an “explanation of a natural phenomenon” it has neither the variables nor the ability to be falsified.

The doctrine doesn’t necessarily work when we’ve got to the limit of our ability to test. Consider string theory, we have no way of actually detecting these tiny strings but the math checks out. The enquiry has been a scientific one… It doesn’t suddenly become pseudoscience because it’s not testable or falsifiable… But it is as a bit of a dead end… At least with current technology. It’s why the string theory isnt always given the label hypothesis, and considered a strong hypothetical idea (that could one day be a theory of everything).

So, falsification isn’t a strict demarcation of science Vs pseudoscience in the way Popper seemingly wanted but it is indicative of whether something is a credible hypothesis or not. If there isn’t a body of supporting scientific information to make deductions and predictions from, the lack of falsifiability would indicate the statement is mere conjecture.

If a hypothesis is a supposition or proposed explanation made on the basis of limited evidence that proposes a relationship between 2 or more variables as a starting point for further investigation and is usually tentative and subject to testing and revision based on new data, what do we call an explanation that does not meet these criteria?

If a proposed explanation does not meet these criteria it might be regarded as a non-falsifiable hypothesis, as it cannot be tested or proven false by any observation or physical experiment. Therefore, it does not count as a scientific hypothesis, but rather as a conjecture, assumption or belief .

If a “hypothesis” is not falsifiable and/or doesn’t have at least 2 quantifiable variables with a proposed relationship, then it also doesn’t fit the paradigm of null and alternative hypothesis.

Examples of non-falsifiable hypotheses are:

Chocolate is always better than vanilla. [subjective] There is a teapot orbiting the Sun between Earth and Mars. [technically untestable] God exists. [metaphysical]

Therefore if anyone talks about these “hypotheses” and suggests there is a null hypothesis in relation to them, they are either wrong or are also changing the definition of null hypothesis.

The question then comes, why would we call an explanation that doesn’t have the variables, predictive qualities, testability or falsifiability a hypothesis at all?

Like there is a difference between a scientific theory and the colloquial use of theory, which just means an idea, there is a difference between all the types of hypothesis mentioned, especially the scientific(causal) hypothesis, and the non-falsifiable hypothesis which is mere conjecture, assumption, or belief.

The main issue here isn’t so much the colloquialism/broad use, as it is the use of technical language in a vague way as if it is the same as a scientific hypothesis, or even one of the other types.

There’s a variety of types of hypothesis, they overlap in places or describe concepts within a hypothesis/hypothesis testing.

When it comes to science, the causal hypothesis (inclusive of its directions and nulls) is what is meant by hypothesis as we are looking for the explanations, causes, how and why from our observations.

The descriptive hypothesis is used within science but is likely better described as “tentative law” as that more accurately describes what it is.

I’ll address why variables, operationalization, predictive qualities etc. are considered fundamentals of a hypothesis and why if a hypothesis is not falsifiable it is not really a hypothesis in my next article.

For now, consider that all the types of hypothesis outside of the colloqualism/broad use contain variables and operate in a way that allows them to have an alternative and null within. If you propose a hypothesis without those clearly defined variables then you don’t have an alternative and null. If you refer to this as a hypothesis, you’re not really using it in the way hypotheses are usually thought of.

Hypothesising something isn’t the same as formulating a hypothesis, though it is what leads there. Much in the same way thinking about what to have for dinner tonight isn’t the same as making dinner.

Hopefully, it’s helped explain hypotheses and we’re left with a better understanding, but failing that I’ve included my references below.

• https://owl.excelsior.edu/research/research-hypotheses/types-of-research-hypotheses/
• https://study.com/learn/lesson/causal-relational-hypotheses-overview-similarities-examples.html
• https://www.simplypsychology.org/what-is-a-hypotheses.html
• https://analyse-it.com/docs/user-guide/multivariate/independence-hypothesis-test
• https://byjus.com/physics/hypothesis/
• https://journals.sagepub.com/doi/10.1177/2515245920970949?icid=int.sj-full-text.similar-articles.3
• http://files.eric.ed.gov/fulltext/EJ1057150.pdf
• Association versus Causation (bu.edu)
• Operationalization – Wikipedia
• Operationalisation | A Guide with Examples, Pros & Cons (scribbr.co.uk)

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Video: Hypothesis | Definition, Types & Examples

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• 0:00 What Is A Hypothesis?
• 0:35 The Purpose Of A Hypothesis
• 1:45 How To Develop A Hypothesis
• 3:00 How To Write A Hypothesis
• 4:25 Examples
• 5:20 Lesson Summary

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What is a hypothesis?

Examples of hypotheses:

As the number of cigarettes a person smokes per day increases, the risk of lung cancer increases. Black cats never get adopted from animal shelters.

A hypothesis can be further broken down into a null hypothesis and an alternative hypothesis . The null hypothesis states that there is no relation and the alternative hypothesis states that there is a relation.

Referring to the first example given above, the null hypothesis would be that there is no relationship between the number of cigarettes a person smokes per day and the risk of lung cancer. The alternative hypothesis is that there is an effect of the number of cigarettes a person smokes per day and the risk of lung cancer OR that the larger the number of cigarettes a person smokes per day, the larger the risk of lung cancer.

A good hypothesis should not only be clear and informative, but it also needs to be measurable.

Hypotheses should be developed after studying the problem or issue as thoroughly as possible, building upon previous knowledge and observations.

Related questions

• What is hypothesis testing in Statistics?
• When would you use a one-sided alternative hypothesis?
• What is the five-step process for hypothesis testing?
• What does it mean if the null hypotheses is rejected?
• What does it mean when a hypothesis is statistically significant?
• What hypothesis test do you use when the population variance is unknown?
• How does sample size affect hypothesis testing?
• What is the purpose of hypothesis testing in statistics?
• A retailer sells 8 brands across 500 locations. What statistical test can be used to determine...

Impact of this question

Inferential Statistics

• Inferential Statistics – Definition, Types, Examples, Formulas
• Observational Studies and Experiments
• Sample and Population
• Sampling Bias
• Sampling Methods
• Confounding Variables
• Causal Conclusions
• Independent and Paired Samples
• Control and Placebo Groups
• Population Distribution, Sample Distribution and Sampling Distribution
• Central Limit Theorem
• Point Estimates
• Confidence Intervals
• Introduction to Bootstrapping
• Bootstrap Confidence Interval
• Paired Samples
• Impact of Sample Size on Confidence Intervals
• Introduction to Hypothesis Testing
• Writing Hypotheses

Hypotheses Test Examples

• Randomization Procedures
• Type I and Type II Errors
• P-value Significance Level
• Issues with Multiple Testing
• Confidence Intervals and Hypothesis Testing
• One Sample Proportion
• One Sample Mean & t Distribution
• Inference for Paired Means
• Inference for Two Independent Proportions
• Inference for Two Independent Means
• Introduction to the F Distribution
• One-way ANOVA hypothesis test
• Two-Way ANOVA
• Chi-Square Goodness of Fit Test
• Chi-Square Test of Independence

Here are some examples of hypothesis test for different types:

Example: Hospital [ Single Mean (µ)]

A hospital wants to know if the average time that patients wait in the emergency room is less than 30 minutes. They take a random sample of 50 patients and find that the average waiting time is 27 minutes with a standard deviation of 5 minutes.

• The null hypothesis (H0) is that the population mean waiting time is equal to 30 minutes. H0: µ = 30
• The alternative hypothesis (Ha) is that it is less than 30 minutes. (Ha): µ < 30

Where: µ is the population mean waiting time in the emergency room for all patients.

They conduct a one-sample t-test to determine if the sample mean waiting time is significantly different from the hypothesized population mean of 30 minutes.

Example: Hospital [Paired Means (µd) ]

A researcher wants to know if a new training program improves employee productivity. They measure the productivity of 20 employees before and after the training program.

• The null hypothesis (H0) is that there is no difference in the mean productivity before and after the training program. H0: µ1 = µ2
• Alternative hypothesis (Ha) is that there is an improvement.  Ha: µ1 < µ2

Where: µ1 is the population mean productivity before the training program µ2 is the population mean productivity after the training program

Same thing can be written in terms of µd, where µd = the mean difference between the before and after scores:

They conduct a paired t-test to determine if there is a significant difference in the mean productivity before and after the training program.

Example: Company [Single Proportion (p) ]

A company wants to know if the proportion of defective products in a batch is greater than 5%. They take a random sample of 200 products and find that 15 of them are defective.

• The null hypothesis (H0) is that the population proportion of defective products is equal to 5%. H0: p = 0.05
• Alternative hypothesis (Ha) is that it is greater than 5%. Ha: p > 0.05

Where: p is the population proportion of defective products in the batch.

They conduct a one-sample proportion test to determine if the sample proportion of defective products is significantly different from the hypothesized population proportion of 5%.

Example: Company [ Difference Between Two Independent Means (µ1-µ2) ]

A researcher wants to know if there is a significant difference in the mean salary between male and female employees. They take a random sample of 50 male employees and 50 female employees and find that the mean salary for males is $50,000 with a standard deviation of $5,000, while the mean salary for females is $45,000 with a standard deviation of $6,000.

The null hypothesis is that there is no difference in the mean salary between males and females, and the alternative hypothesis is that there is a difference.

• H0: µ1 = µ2
• Ha: µ1 ≠ µ2

Where: µ1 is the mean salary of male employees µ2 is the mean salary of female employees

They conduct a two-sample t-test to determine if there is a significant difference in the mean salary between males and females.

Example: Company [Difference Between Two Proportions (p1-p2) ]

A company wants to know if there is a significant difference in the proportion of customers who prefer their product compared to their competitor’s product. They survey 500 customers and find that 300 prefer their product, while 200 prefer the competitor’s product.

The null hypothesis is that there is no difference in the proportion of customers who prefer their product and the competitor’s product, and the alternative hypothesis is that there is a difference.

• H0: p1 = p2
• Ha: p1 ≠ p2

Where: p1 is the population proportion of customers who prefer the company’s product p2 is the population proportion of customers who prefer the competitor’s product

They conduct a two-sample proportion test to determine if there is a significant difference in the proportion of customers who prefer their product compared to their competitor’s product.

Example: Person’s age vs income [Simple Linear Regression Slope (β) ]

A researcher wants to know if there is a significant linear relationship between a person’s age and their income. They collect data on the age and income of 100 people and conduct a simple linear regression analysis.

The null hypothesis is that there is no significant linear relationship between age and income, and the alternative hypothesis is that there is a significant linear relationship.

Where: β is the slope coefficient in the simple linear regression model between age and income.

They conduct a simple linear regression analysis to determine if there is a significant linear relationship between age and income.

Example: person’s height vs weight [Correlation (ρ) ]

A researcher wants to know if there is a significant correlation between a person’s height and weight. They collect data on the height and weight of 50 people and calculate the correlation coefficient. The null hypothesis is that there is no significant correlation  between height and  weight and the alternative hypothesis is that there is a significant correlation.

Where: ρ is the population correlation coefficient between height and weight.

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What Is a Hypothesis? Essay Example

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Study Relaxation Exercises

A researcher interested in the idea that people who engage in conscious relaxation (like meditation or relaxation exercises) experience fewer age-related aches and pains than people who do not consciously relax. Create a working hypothesis for this research question. Be sure to be specific about the IV and DV of this study.

The dependent variable is age, independent variables men with aches and pains, men people who engage in conscious relaxation, men who engage in relaxation exercises. This example meets the definition of hypothesis.” A hypothesis is a tentative statement about the relationship between two variables.” (Cherry, 2015). The null hypothesis the opposite of the basis of the hypothesis, the null is the alternative (Shuttlework, 2015).

Hypothesis: There is a correlation and a statistical difference between the age of men that engage in conscious relaxation and relaxation exercises that have fewer age related aches and pains

Null: There is a no correlation or statistical significance between the age of men that engage in conscious relaxation and relaxation exercises

Long Term Incarceration

A researcher is interested in the psychological ramifications of long-term incarceration hypothesizing that the psychological risks to inmates in many cases constitute cruel and unusual punishment. To conduct her study, she meets with the warden of a nearby prison and asks if she can interview the inmates. The warden gives her permission and tells the inmates they will be participating in the interviews. Is this study ethical? Why or why not?

The studies are not ethical because the prisoner have the right to choose to participant concerning personal, confidential and prison information. This is unethical because the consideration of all the people that might be involved such as other inmates willing or unwilling, guards that are rough and cruel and the inmates safety who agrees to talk about the incidents inside the prison. The prisoners may lose their public rights and benefits but they do not lose their freedom of privacy and choice. Finally, the warden does not have the power or right to subject prisons to a study that has not been approved by the prisons and by the state.

Workday Breaks

A researcher hypothesizes that people who take a scheduled break between lunch and the end of the workday are more satisfied with their job and are more productive at the end of the day than people who do not take a break. Is this a good hypothesis? Why or why not?

Yes, this hypothesis is sound because people who do not take breaks over time will not be more productive because they become worn out from the lack of breaks and given the body a chance to rest or recuperate. The workers that take breaks are more alert, refreshed and mental alert after giving the mind and body a break.

Voters Attitudes

A researcher finds that participants who live in urban areas generally vote democratic and those who live in rural areas tend to vote republican. He claims that living in crowded conditions causes people to favor democratic attitudes and vice versa for republican attitudes. Is this a valid conclusion? Why or why not?

This is not a valid conclusion. The conclusion is not valid because people that vote regardless of urban or rural do not dictate republican and democrat. This would mean that all people live in city vote republican and people in the rural area vote democratic is not true. A persons personal beliefs do help make a decision about voting republican and democrats but the location of a person’s household, does not dictate how they vote.

Human Societies

A researcher is interested in the development of human societies. She hypothesizes that many human societies are centered around family groups because when humans were hunter-gatherers they wanted to protect their own bloodline and not another person’s. Is this a good hypothesis? Why or why not?

This would be a good hypothesis because humanity has been based on the family unit that work and live to protect their families. There is the primary reason the family group developments the society by building regions on the based on kinship.

A group of researchers takes the view that the mind works similarly to a computer – data are input to the mind through the sense organs, these data are processed, and the output is something like “thought” or “perception.” These researchers hypothesize that data processing in the mind takes time. Write a hypothesis that can be used to test the claim that these mental processes take time.

Yes, this premise would be an excellent project to complete because the mind is like a data processor. The project can simulate the computer works with utilize the same examples of how the mind works during the mental process.

Judgement Error

A researcher hypothesizes that participants who are informed about the fundamental attribution error (the tendency to blame another’s circumstances on dispositional factors while largely disregarding situational factors) will be less likely to make the error in their judgments of other people’s situations. Is this a good hypothesis? Why or why not?

No this would not be good hypotheses because the study does not incorporate situational issues that may help develop the hypothesis based on the different circumstances that would change the results or altered the judgement.

Cherry, K. (2015).What is a hypothesis. Retrieved March 26, 2015 from http://psychology.about.com/od/hindex/g/hypothesis.htm

Shuttleworth, M. (2015). Null hypothesis. Retrieved March 27, 2015 from https://explorable.com/null-hypothesis

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