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What is a Hypothesis – Types, Examples and Writing Guide

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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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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5 Characteristics of a Good Hypothesis: A Guide for Researchers

• by Brian Thomas
• October 10, 2023

Are you a curious soul, always seeking answers to the whys and hows of the world? As a researcher, formulating a hypothesis is a crucial first step towards unraveling the mysteries of your study. A well-crafted hypothesis not only guides your research but also lays the foundation for drawing valid conclusions. But what exactly makes a hypothesis a good one? In this blog post, we will explore the five key characteristics of a good hypothesis that every researcher should know.

Here, we will delve into the world of hypotheses, covering everything from their types in research to understanding if they can be proven true. Whether you’re a seasoned researcher or just starting out, this blog post will provide valuable insights on how to craft a sound hypothesis for your study. So let’s dive in and uncover the secrets to formulating a hypothesis that stands strong amidst the scientific rigor!

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5 Characteristics of a Good Hypothesis

Clear and specific.

A good hypothesis is like a GPS that guides you to the right destination. It needs to be clear and specific so that you know exactly what you’re testing. Avoid vague statements or general ideas. Instead, focus on crafting a hypothesis that clearly states the relationship between variables and the expected outcome. Clarity is key, my friend!

Testable and Falsifiable

A hypothesis might sound great in theory, but if you can’t test it or prove it wrong, then it’s like chasing unicorns. A good hypothesis should be testable and falsifiable – meaning there should be a way to gather evidence to support or refute it. Don’t be afraid to challenge your hypothesis and put it to the test. Only when it can be proven false can it truly be considered a good hypothesis.

Based on Existing Knowledge

Imagine trying to build a Lego tower without any Lego bricks. That’s what it’s like to come up with a hypothesis that has no basis in existing knowledge. A good hypothesis is grounded in previous research, theories, or observations. It shows that you’ve done your homework and understand the current state of knowledge in your field. So, put on your research hat and gather those building blocks for a solid hypothesis!

Specific Predictions

No, we’re not talking about crystal ball predictions or psychic abilities here. A good hypothesis includes specific predictions about what you expect to happen. It’s like making an educated guess based on your understanding of the variables involved. These predictions help guide your research and give you something concrete to look for. So, put on those prediction goggles, my friend, and let’s get specific!

Relevant to the Research Question

A hypothesis is a road sign that points you in the right direction. But if it’s not relevant to your research question, then you might end up in a never-ending detour. A good hypothesis aligns with your research question and addresses the specific problem or phenomenon you’re investigating. Keep your focus on the main topic and avoid getting sidetracked by shiny distractions. Stay relevant, my friend, and you’ll find the answers you seek!

And there you have it: the five characteristics of a good hypothesis. Remember, a good hypothesis is clear, testable, based on existing knowledge, makes specific predictions, and is relevant to your research question. So go forth, my friend, and hypothesize your way to scientific discovery!

FAQs: Characteristics of a Good Hypothesis

In the realm of scientific research, a hypothesis plays a crucial role in formulating and testing ideas. A good hypothesis serves as the foundation for an experiment or study, guiding the researcher towards meaningful results. In this FAQ-style subsection, we’ll explore the characteristics of a good hypothesis, their types, formulation, and more. So let’s dive in and unravel the mysteries of hypothesis-making!

What Are Two Important Characteristics of a Good Hypothesis

A good hypothesis possesses two important characteristics:

Testability : A hypothesis must be testable to determine its validity. It should be formulated in a way that allows researchers to design and conduct experiments or gather data for analysis. For example, if we hypothesize that “drinking herbal tea reduces stress,” we can easily test it by conducting a study with a control group and a group drinking herbal tea.

Falsifiability : Falsifiability refers to the potential for a hypothesis to be proven wrong. A good hypothesis should make specific predictions that can be refuted or supported by evidence. This characteristic ensures that hypotheses are based on empirical observations rather than personal opinions. For instance, the hypothesis “all swans are white” can be falsified by discovering a single black swan.

What Are the Types of Hypothesis in Research

In research, there are three main types of hypotheses:

Null Hypothesis (H0) : The null hypothesis is a statement of no effect or relationship. It assumes that there is no significant difference between variables or no effect of a treatment. Researchers aim to reject the null hypothesis in favor of an alternative hypothesis.

Alternative Hypothesis (HA or H1) : The alternative hypothesis is the opposite of the null hypothesis. It asserts that there is a significant difference between variables or an effect of a treatment. Researchers seek evidence to support the alternative hypothesis.

Directional Hypothesis : A directional hypothesis predicts the specific direction of the relationship or difference between variables. For example, “increasing exercise duration will lead to greater weight loss.”

Can a Hypothesis Be Proven True

In scientific research, hypotheses are not proven true; they are supported or rejected based on empirical evidence . Even if a hypothesis is supported by multiple studies, new evidence could arise that contradicts it. Scientific knowledge is always subject to revision and refinement. Therefore, the goal is to gather enough evidence to either support or reject a hypothesis, rather than proving it absolutely true.

What Are the Six Parts of a Hypothesis

A hypothesis typically consists of six essential parts:

Research Question : A clear and concise question that the hypothesis seeks to answer.

Variables : Identification of the independent (manipulated) and dependent (measured) variables involved in the hypothesis.

Population : The specific group or individuals the hypothesis is concerned with.

Relationship or Comparison : The expected relationship or difference between variables, often indicated by directional terms like “more,” “less,” “higher,” or “lower.”

Predictability : A statement of the predicted outcome or result based on the relationship between variables.

Testability : The ability to design an experiment or gather data to support or reject the hypothesis.

How Do You Start a Hypothesis Sentence

When starting a hypothesis sentence, it is essential to use clear and concise language to express your ideas. A common approach is to use the phrase “If…then…” to establish the conditional relationship between variables. For example:

• If [independent variable], then [dependent variable] because [explanation of expected relationship].

This structure allows for a straightforward and logical formulation of the hypothesis.

What Are Examples of Hypotheses

Here are a few examples of well-formulated hypotheses:

If exposure to sunlight increases, then plants will grow taller because sunlight is necessary for photosynthesis.

If students receive praise for good grades, then their motivation to excel will increase because they seek recognition and approval.

If the dose of a painkiller is increased, then the relief from pain will last longer because a higher dosage has a prolonged effect.

What Are the Five Key Elements to a Good Hypothesis

A good hypothesis should include the following five key elements:

Clarity : The hypothesis should be clear and specific, leaving no room for interpretation.

Testability : It should be possible to test the hypothesis through experimentation or data collection.

Relevance : The hypothesis should be directly tied to the research question or problem being investigated.

Specificity : It must clearly state the relationship or difference between variables being studied.

Falsifiability : The hypothesis should make predictions that can be refuted or supported by empirical evidence.

What Makes a Good Hypothesis in a Research Paper

In a research paper, a good hypothesis should have the following characteristics:

Relevance : It must directly relate to the research topic and address the objectives of the study.

Clarity : The hypothesis should be concise and precisely worded to avoid confusion.

Unambiguous : It must leave no room for multiple interpretations or ambiguity.

Logic : The hypothesis should be based on rational and logical reasoning, considering existing theories and observations.

Empirical Support : Ideally, the hypothesis should be supported by prior empirical evidence or strong theoretical justifications.

Is a Hypothesis Always a Question

No, a hypothesis is not always in the form of a question. While some hypotheses can take the form of a question, others may be statements asserting a relationship or difference between variables. The form of a hypothesis depends on the research question being addressed and the researcher’s preferred style of expression.

What Are the Three Things Needed for a Good Hypothesis

For a hypothesis to be considered good, it must fulfill the following three criteria:

Testability : The hypothesis should be formulated in a way that allows for empirical testing through experimentation or data collection.

Falsifiability : It must make specific predictions that can be potentially refuted or supported by evidence.

Relevance : The hypothesis should directly address the research question or problem being investigated.

What Are the Four Components to a Good Hypothesis

A good hypothesis typically consists of four components:

Independent Variable : The variable being manipulated or controlled by the researcher.

Dependent Variable : The variable being measured or observed to determine the effect of the independent variable.

Directionality : The predicted relationship or difference between the independent and dependent variables.

Population : The specific group or individuals to which the hypothesis applies.

How Do You Formulate a Hypothesis

To formulate a hypothesis, follow these steps:

Identify the Research Topic : Clearly define the area or phenomenon you want to study.

Conduct Background Research : Review existing literature and research to gain knowledge about the topic.

Formulate a Research Question : Ask a clear and focused question that you want to answer through your hypothesis.

State the Null and Alternative Hypotheses : Develop a null hypothesis to assume no effect or relationship, and an alternative hypothesis to propose a significant effect or relationship.

Decide on Variables and Relationships : Determine the independent and dependent variables and the predicted relationship between them.

Refine and Test : Refine your hypothesis, ensuring it is clear, testable, and falsifiable. Then, design experiments or gather data to support or reject it.

What Is a Characteristic of a Hypothesis MCQ

Multiple-choice questions (MCQ) regarding the characteristics of a hypothesis often assess knowledge on the testability and falsifiability of hypotheses. They may ask about the criteria that distinguish a good hypothesis from a poor one or the importance of making specific predictions. Remember to choose answers that emphasize the empirical and testable nature of hypotheses.

What Five Criteria Must Be Satisfied for a Hypothesis to Be Scientific

For a hypothesis to be considered scientific, it must satisfy the following five criteria:

Testability : The hypothesis must be formulated in a way that allows it to be tested through experimentation or data collection.

Falsifiability : It should make specific predictions that can be potentially refuted or supported by empirical evidence.

Empirical Basis : The hypothesis should be based on empirical observations or existing theories and knowledge.

Relevance : It must directly address the research question or problem being investigated.

Objective : A scientific hypothesis should be free from personal biases or subjective opinions, focusing on objective observations and analysis.

What Are the Steps of Theory Development in Scientific Methods

In scientific methods, theory development typically involves the following steps:

Observation : Identifying a phenomenon or pattern worthy of investigation through observation or empirical data.

Formulation of a Hypothesis : Constructing a hypothesis that explains the observed phenomena or predicts a relationship between variables.

Data Collection : Gathering relevant data through experiments, surveys, observations, or other research methods.

Analysis : Analyzing the collected data to evaluate the hypothesis’s predictions and determine their validity.

Revision and Refinement : Based on the analysis, refining the hypothesis, modifying the theory, or formulating new hypotheses for further investigation.

Which of the Following Makes a Good Hypothesis

A good hypothesis is characterized by:

Testability : The ability to form experiments or gather data to support or refute the hypothesis.

Falsifiability : The potential for the hypothesis’s predictions to be proven wrong based on empirical evidence.

Clarity : A clear and concise statement or question that leaves no room for ambiguity.

Relevancy : Directly addressing the research question or problem at hand.

Remember, it is important to select the option that encompasses all these characteristics.

What Are the Characteristics of a Good Hypothesis

A good hypothesis possesses several characteristics, such as:

Testability : It should allow for empirical testing through experiments or data collection.

Falsifiability : The hypothesis should make specific predictions that can be potentially refuted or supported by evidence.

Clarity : It must be clearly and precisely formulated, leaving no room for ambiguity or multiple interpretations.

Relevance : The hypothesis should directly relate to the research question or problem being investigated.

What Is the Five-Step p-value Approach to Hypothesis Testing

The five-step p-value approach is a commonly used framework for hypothesis testing:

Step 1: Formulating the Hypotheses : The null hypothesis (H0) assumes no effect or relationship, while the alternative hypothesis (HA) proposes a significant effect or relationship.

Step 2: Setting the Significance Level : Decide on the level of significance (α), which represents the probability of rejecting the null hypothesis when it is true. The commonly used level is 0.05 (5%).

Step 3: Collecting Data and Performing the Test : Acquire and analyze the data, calculating the test statistic and the corresponding p-value.

Step 4: Comparing the p-value with the Significance Level : If the p-value is less than the significance level (α), reject the null hypothesis. Otherwise, fail to reject the null hypothesis.

Step 5: Drawing Conclusions : Based on the comparison in Step 4, interpret the results and draw conclusions about the hypothesis.

What Are the Stages of Hypothesis

The stages of hypothesis generally include:

Observation : Identifying a pattern, phenomenon, or research question that warrants investigation.

Formulation : Developing a hypothesis that explains or predicts the relationship or difference between variables.

Testing : Collecting data, designing experiments, or conducting studies to gather evidence supporting or refuting the hypothesis.

Analysis : Assessing the collected data to determine whether the results support or reject the hypothesis.

Conclusion : Drawing conclusions based on the analysis and making further iterations, refinements, or new hypotheses for future research.

What Is a Characteristic of a Good Hypothesis

A characteristic of a good hypothesis is its ability to make specific predictions about the relationship or difference between variables. Good hypotheses avoid vague statements and clearly articulate the expected outcomes. By doing so, researchers can design experiments or gather data that directly test the predictions, leading to meaningful results.

How Do You Write a Good Hypothesis Example

To write a good hypothesis example, follow these guidelines:

If possible, use the “If…then…” format to express a conditional relationship between variables.

Be clear and concise in stating the variables involved, the predicted relationship, and the expected outcome.

Ensure the hypothesis is testable, meaning it can be evaluated through experiments or data collection.

For instance, consider the following example:

If students study for longer periods of time, then their test scores will improve because increased study time allows for better retention of information and increased proficiency.

What Is the Difference Between Hypothesis and Hypotheses

The main difference between a hypothesis and hypotheses lies in their grammatical number. A hypothesis refers to a single statement or proposition that is formulated to explain or predict the relationship between variables. On the other hand, hypotheses is the plural form of the term hypothesis, commonly used when multiple statements or propositions are proposed and tested simultaneously.

What Is a Good Hypothesis Statement

A good hypothesis statement exhibits the following qualities:

Clarity : It is written in clear and concise language, leaving no room for confusion or ambiguity.

Testability : The hypothesis should be formulated in a way that enables testing through experiments or data collection.

Specificity : It must clearly state the predicted relationship or difference between variables.

By adhering to these criteria, a good hypothesis statement guides research efforts effectively.

What Is Not a Characteristic of a Good Hypothesis

A characteristic that does not align with a good hypothesis is subjectivity . A hypothesis should be objective, based on empirical observations or existing theories, and free from personal bias. While personal interpretations and opinions can inspire the formulation of a hypothesis, it must ultimately rely on objective observations and be open to empirical testing.

By now, you’ve gained insights into the characteristics of a good hypothesis, including testability, falsifiability, clarity,

• characteristics
• falsifiable
• good hypothesis
• hypothesis testing
• null hypothesis
• observations
• scientific rigor

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The Craft of Writing a Strong Hypothesis

Table of Contents

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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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.

Table of contents

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

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 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).

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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What Are the Elements of a Good Hypothesis?

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A hypothesis is an educated guess or prediction of what will happen. In science, a hypothesis proposes a relationship between factors called variables. A good hypothesis relates an independent variable and a dependent variable. The effect on the dependent variable depends on or is determined by what happens when you change the independent variable . While you could consider any prediction of an outcome to be a type of hypothesis, a good hypothesis is one you can test using the scientific method. In other words, you want to propose a hypothesis to use as the basis for an experiment.

Cause and Effect or 'If, Then' Relationships

A good experimental hypothesis can be written as an if, then statement to establish cause and effect on the variables. If you make a change to the independent variable, then the dependent variable will respond. Here's an example of a hypothesis:

If you increase the duration of light, (then) corn plants will grow more each day.

The hypothesis establishes two variables, length of light exposure, and the rate of plant growth. An experiment could be designed to test whether the rate of growth depends on the duration of light. The duration of light is the independent variable, which you can control in an experiment . The rate of plant growth is the dependent variable, which you can measure and record as data in an experiment.

Key Points of Hypothesis

When you have an idea for a hypothesis, it may help to write it out in several different ways. Review your choices and select a hypothesis that accurately describes what you are testing.

• Does the hypothesis relate an independent and dependent variable? Can you identify the variables?
• Can you test the hypothesis? In other words, could you design an experiment that would allow you to establish or disprove a relationship between the variables?
• Would your experiment be safe and ethical?
• Is there a simpler or more precise way to state the hypothesis? If so, rewrite it.

What If the Hypothesis Is Incorrect?

It's not wrong or bad if the hypothesis is not supported or is incorrect. Actually, this outcome may tell you more about a relationship between the variables than if the hypothesis is supported. You may intentionally write your hypothesis as a null hypothesis or no-difference hypothesis to establish a relationship between the variables.

For example, the hypothesis:

The rate of corn plant growth does not depend on the duration of light.

This can be tested by exposing corn plants to different length "days" and measuring the rate of plant growth. A statistical test can be applied to measure how well the data support the hypothesis. If the hypothesis is not supported, then you have evidence of a relationship between the variables. It's easier to establish cause and effect by testing whether "no effect" is found. Alternatively, if the null hypothesis is supported, then you have shown the variables are not related. Either way, your experiment is a success.

Need more examples of how to write a hypothesis ? Here you go:

• If you turn out all the lights, you will fall asleep faster. (Think: How would you test it?)
• If you drop different objects, they will fall at the same rate.
• If you eat only fast food, then you will gain weight.
• If you use cruise control, then your car will get better gas mileage.
• If you apply a top coat, then your manicure will last longer.
• If you turn the lights on and off rapidly, then the bulb will burn out faster.
• What Are Examples of a Hypothesis?
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• What Is a Hypothesis? (Science)
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• Six Steps of the Scientific Method
• Scientific Method Flow Chart
• Null Hypothesis Examples
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• What Is a Controlled Experiment?
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Writing a Research Proposal

Parts of a research proposal, prosana model, introduction, research question, methodology.

• Structure of a Research Proposal
• Common Proposal Writing Mistakes
• Proposal Writing Resources

A research proposal's purpose is to capture the evaluator's attention, demonstrate the study's potential benefits, and prove that it is a logical and consistent approach (Van Ekelenburg, 2010).  To ensure that your research proposal contains these elements, there are several aspects to include in your proposal (Al-Riyami, 2008):

• Objective(s)
• Variables (independent and dependent)
• Research Question and/or hypothesis

Details about what to include in each element are included in the boxes below.  Depending on the topic of your study, some parts may not apply to your proposal.  You can also watch the video below for a brief overview about writing a successful research proposal.

Van Ekelenburg (2010) uses the PROSANA Model to guide researchers in developing rationale and justification for their research projects.  It is an acronym that connects the problem, solution, and benefits of a particular research project.  It is an easy way to remember the critical parts of a research proposal and how they relate to one another.  It includes the following letters (Van Ekelenburg, 2010):

• Problem: Describing the main problem that the researcher is trying to solve.
• Root causes: Describing what is causing the problem.  Why is the topic an issue?
• fOcus: Narrowing down one of the underlying causes on which the researcher will focus for their research project.
• Solutions: Listing potential solutions or approaches to fix to the problem.  There could be more than one.
• Approach: Selecting the solution that the researcher will want to focus on.
• Novelty: Describing how the solution will address or solve the problem.
• Arguments: Explaining how the proposed solution will benefit the problem.

Research proposal titles should be concise and to the point, but informative.  The title of your proposal may be different from the title of your final research project, but that is completely normal!  Your findings may help you come up with a title that is more fitting for the final project.  Characteristics of good proposal titles are (Al-Riyami, 2008):

• Catchy: It catches the reader's attention by peaking their interest.
• Positive: It spins your project in a positive way towards the reader.
• Transparent: It identifies the independent and dependent variables.

It is also common for proposal titles to be very similar to your research question, hypothesis, or thesis statement (Locke et al., 2007).

An abstract is a brief summary (about 300 words) of the study you are proposing.  It includes the following elements (Al-Riyami, 2008):

• Your primary research question(s).
• Hypothesis or main argument.
• Method you will use to complete the study.  This may include the design, sample population, or measuring instruments that you plan to use.

Our guide on writing summaries may help you with this step.

The purpose of the introduction is to give readers background information about your topic.  it gives the readers a basic understanding of your topic so that they can further understand the significance of your proposal.  A good introduction will explain (Al-Riyami, 2008):

• How it relates to other research done on the topic
• Why your research is significant to the field
• The relevance of your study

Your research objectives are the desired outcomes that you will achieve from the research project.  Depending on your research design, these may be generic or very specific.  You may also have more than one objective (Al-Riyami, 2008).

• General objectives are what the research project will accomplish
• Specific objectives relate to the research questions that the researcher aims to answer through the study.

Be careful not to have too many objectives in your proposal, as having too many can make your project lose focus.  Plus, it may not be possible to achieve several objectives in one study.

This section describes the different types of variables that you plan to have in your study and how you will measure them.  According to Al-Riyami (2008), there are four types of research variables:

• Independent:  The person, object, or idea that is manipulated by the researcher.
• Dependent:  The person, object, or idea whose changes are dependent upon the independent variable.  Typically, it is the item that the researcher is measuring for the study.
• Confounding/Intervening:  Factors that may influence the effect of the independent variable on the dependent variable.  These include physical and mental barriers.  Not every study will have intervening variables, but they should be studied if applicable.
• Background:   Factors that are relevant to the study's data and how it can be generalized.  Examples include demographic information such as age, sex, and ethnicity.

Your research proposal should describe each of your variables and how they relate to one another.  Depending on your study, you may not have all four types of variables present.  However, there will always be an independent and dependent variable.

A research question is the main piece of your research project because it explains what your study will discover to the reader.  It is the question that fuels the study, so it is important for it to be precise and unique.  You do not want it to be too broad, and it should identify a relationship between two variables (an independent and a dependent) (Al-Riyami, 2008).  There are six types of research questions (Academic Writer, n.d.):

• Example: "Do people get nervous before speaking in front of an audience?"
• Example: "What are the study habits of college freshmen at Tiffin University?"
• Example: "What primary traits create a successful romantic relationship?"
• Example: "Is there a relationship between a child's performance in school and their parents' socioeconomic status?"
• Example: "Are high school seniors more motivated than high school freshmen?"
• Example: "Do news media outlets impact a person's political opinions?"

For more information on the different types of research questions, you can view the "Research Questions and Hypotheses" tutorial on Academic Writer, located below.  If you are unfamiliar with Academic Writer, we also have a tutorial on using the database located below.

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If you know enough about your research topic that you believe a particular outcome may occur as a result of the study, you can include a hypothesis (thesis statement) in your proposal.  A hypothesis is a prediction that you believe will be the outcome of your study.  It explains what you think the relationship will be between the independent and dependent variable (Al-Riyami, 2008).  It is ok if the hypothesis in your proposal turns out to be incorrect, because it is only a prediction!  If you are writing a proposal in the humanities, you may be writing a thesis statement instead of a hypothesis.  A thesis presents the main argument of your research project and leads to corresponding evidence to support your argument.

Hypotheses vs. Theories

Hypotheses are different from theories in that theories represent general principles and sets of rules that explain different phenomena.  They typically represent large areas of study because they are applicable to anything in a particular field.  Hypotheses focus on specific areas within a field and are educated guesses, meaning that they have the potential to be proven wrong (Academic Writer, n.d.).  Because of this, hypotheses can also be formed from theories.

For more information on writing effective thesis statements, you can view our guide on writing thesis statements below.

In a research proposal, you must thoroughly explain how you will conduct your study.  This includes things such as (Al-Riyami, 2008):

• Research design:  What research approach will your study take?  Will it be quantitative or qualitative?
• Research subjects/participants:  Who will be participating in your study?  Does your study require human participants?  How will you determine who to study?
• Sample size:  How many participants will your study require?  If you are not using human participants, how much of the sample will you be studying?
• Timeline:  A proposed list of the general tasks and events that you plan to complete the study.  This will include a time frame for each task/event and the order in which they will be completed.
• Interventions:  If you plan on using anything on human participants for the study, you must include information it here.  This is especially important if you plan on using any substances on human subjects.
• Ethical issues:  Are there any potential ethical issues surrounding this study?
• Potential limitations:  Are there any limitations that could skew the data and findings from your study?
• Appendixes:  If you need to present any consent forms, interview questions, surveys, questionnaires, or other items that will be used in your study, you should include samples of each item with an appendix to reference them.  If you are using a copyrighted document, you may need written permission from the original creator to use it in your study.  A copy of the written permission should be included in your proposal.
• Setting:  Where will you be conducting the study?
• Study instruments:  What measuring tools or computer software will you be using to collect data?  How will you collect the data?
• How you will analyze the data:  What strategies or tools will you use to analyze the data you collect?
• Quality control:  Will you have precautions in place to ensure that the study is conducted consistently and that outside factors will not skew the data?
• Budget:  What type of funding will you need for your study?  This will include the funds needed to afford measuring tools, software, etc.
• How you will share the study's findings:  What will you plan to do with the findings?
• Significance of the study: How will your study expand on existing knowledge of the subject area?

For more information on research methodologies, you can view our guide on research methods and methodologies below.

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What Is the Function of the Hypothesis?

Types of Observation in the Scientific Method

Hypotheses are the questions scientists ask as they use the scientific method to understand the world. People use the process of formulating then attempting to disprove a hypothesis in their everyday lives as well. The function of the hypothesis is to give structure to the process of understanding how the world works.

Identification

A hypothesis is an educated guess, based on the probability of an outcome. Scientists formulate hypotheses after they understand all the current research on their subject. Hypotheses specify the relationship between at least two variables, and are testable. For a hypothesis to function properly, other scientists must be able to reproduce the results that prove or disprove it. Two types of hypotheses exist: a descriptive hypothesis asks a question, and a directional hypothesis makes a statement.

Scientific Method

The scientific method is the process by which hypotheses function. Scientists use the scientific method to, over time, form an accurate picture of the world. The scientific method attempts to remove the scientist's bias from the research. The four parts of the scientific method are observation and description, formulation of a hypothesis, use of the hypothesis for prediction and performance of testing of the hypothesis. Scientists use the scientific method to disprove hypotheses, rather than prove them. It they cannot be disproved, the hypotheses over time become accepted theories.

Experiments

The most important function hypotheses perform is providing the framework for testing and experimentation. Scientists formulate a hypothesis, or ask a question, about a certain phenomenon and how it relates to other aspects of the world. Then they devise ways to try to disprove their theory as to the answer. For instance, if a scientist made a hypothesis that what goes up must come down, he would test it by throwing many items in the air to see if they do come down. Because scientists cannot test every single possible item for this theory, hypotheses are never proven. However, after many scientists have experimented with the hypothesis, it becomes accepted scientific theory.

Formulating Hypotheses

Scientists make a hypothesis by comparing the phenomenon being studied to another phenomenon. For instance, in the real world, a person might decide that her house is cold because a window is open. She would test this theory by checking the windows. If the windows are closed, then that hypothesis is proven false, and another is formed when the person decides that her house is probably cold because the furnace isn't working properly. The process of forming and disproving hypotheses continues until a person makes a hypothesis that cannot be disproved.

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What is Hypothesis? Definition, Meaning, Characteristics, Sources

• Post last modified: 10 January 2022
• Reading time: 18 mins read
• Post category: Research Methodology

• What is Hypothesis?

Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.

As an example, if we want to explore whether using a specific teaching method at school will result in better school marks (research question), the hypothesis could be that the mean school marks of students being taught with that specific teaching method will be higher than of those being taught using other methods.

In this example, we stated a hypothesis about the expected differences between groups. Other hypotheses may refer to correlations between variables.

Table of Content

• 1 What is Hypothesis?
• 2 Hypothesis Definition
• 3 Meaning of Hypothesis
• 4.1 Conceptual Clarity
• 4.2 Need of empirical referents
• 4.3 Hypothesis should be specific
• 4.4 Hypothesis should be within the ambit of the available research techniques
• 4.5 Hypothesis should be consistent with the theory
• 4.6 Hypothesis should be concerned with observable facts and empirical events
• 4.7 Hypothesis should be simple
• 5.1 Observation
• 5.2 Analogies
• 5.4 State of Knowledge
• 5.5 Culture
• 5.6 Continuity of Research
• 6.1 Null Hypothesis
• 6.2 Alternative Hypothesis

Thus, to formulate a hypothesis, we need to refer to the descriptive statistics (such as the mean final marks), and specify a set of conditions about these statistics (such as a difference between the means, or in a different example, a positive or negative correlation). The hypothesis we formulate applies to the population of interest.

The null hypothesis makes a statement that no difference exists (see Pyrczak, 1995, pp. 75-84).

Hypothesis Definition

A hypothesis is ‘a guess or supposition as to the existence of some fact or law which will serve to explain a connection of facts already known to exist.’ – J. E. Creighton & H. R. Smart

Hypothesis is ‘a proposition not known to be definitely true or false, examined for the sake of determining the consequences which would follow from its truth.’ – Max Black

Hypothesis is ‘a proposition which can be put to a test to determine validity and is useful for further research.’ – W. J. Goode and P. K. Hatt

A hypothesis is a proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined. – Webster’s New International Dictionary of the English Language (1956)

Meaning of Hypothesis

From the above mentioned definitions of hypothesis, its meaning can be explained in the following ways.

• At the primary level, a hypothesis is the possible and probable explanation of the sequence of happenings or data.
• Sometimes, hypothesis may emerge from an imagination, common sense or a sudden event.
• Hypothesis can be a probable answer to the research problem undertaken for study. 4. Hypothesis may not always be true. It can get disproven. In other words, hypothesis need not always be a true proposition.
• Hypothesis, in a sense, is an attempt to present the interrelations that exist in the available data or information.
• Hypothesis is not an individual opinion or community thought. Instead, it is a philosophical means which is to be used for research purpose. Hypothesis is not to be considered as the ultimate objective; rather it is to be taken as the means of explaining scientifically the prevailing situation.

The concept of hypothesis can further be explained with the help of some examples. Lord Keynes, in his theory of national income determination, made a hypothesis about the consumption function. He stated that the consumption expenditure of an individual or an economy as a whole is dependent on the level of income and changes in a certain proportion.

Later, this proposition was proved in the statistical research carried out by Prof. Simon Kuznets. Matthus, while studying the population, formulated a hypothesis that population increases faster than the supply of food grains. Population studies of several countries revealed that this hypothesis is true.

Validation of the Malthus’ hypothesis turned it into a theory and when it was tested in many other countries it became the famous Malthus’ Law of Population. It thus emerges that when a hypothesis is tested and proven, it becomes a theory. The theory, when found true in different times and at different places, becomes the law. Having understood the concept of hypothesis, few hypotheses can be formulated in the areas of commerce and economics.

• Population growth moderates with the rise in per capita income.
• Sales growth is positively linked with the availability of credit.
• Commerce education increases the employability of the graduate students.
• High rates of direct taxes prompt people to evade taxes.
• Good working conditions improve the productivity of employees.
• Advertising is the most effecting way of promoting sales than any other scheme.
• Higher Debt-Equity Ratio increases the probability of insolvency.
• Economic reforms in India have made the public sector banks more efficient and competent.
• Foreign direct investment in India has moved in those sectors which offer higher rate of profit.
• There is no significant association between credit rating and investment of fund.

Characteristics of Hypothesis

Not all the hypotheses are good and useful from the point of view of research. It is only a few hypotheses satisfying certain criteria that are good, useful and directive in the research work undertaken. The characteristics of such a useful hypothesis can be listed as below:

Conceptual Clarity

Need of empirical referents, hypothesis should be specific, hypothesis should be within the ambit of the available research techniques, hypothesis should be consistent with the theory, hypothesis should be concerned with observable facts and empirical events, hypothesis should be simple.

The concepts used while framing hypothesis should be crystal clear and unambiguous. Such concepts must be clearly defined so that they become lucid and acceptable to everyone. How are the newly developed concepts interrelated and how are they linked with the old one is to be very clear so that the hypothesis framed on their basis also carries the same clarity.

A hypothesis embodying unclear and ambiguous concepts can to a great extent undermine the successful completion of the research work.

A hypothesis can be useful in the research work undertaken only when it has links with some empirical referents. Hypothesis based on moral values and ideals are useless as they cannot be tested. Similarly, hypothesis containing opinions as good and bad or expectation with respect to something are not testable and therefore useless.

For example, ‘current account deficit can be lowered if people change their attitude towards gold’ is a hypothesis encompassing expectation. In case of such a hypothesis, the attitude towards gold is something which cannot clearly be described and therefore a hypothesis which embodies such an unclean thing cannot be tested and proved or disproved. In short, the hypothesis should be linked with some testable referents.

For the successful conduction of research, it is necessary that the hypothesis is specific and presented in a precise manner. Hypothesis which is general, too ambitious and grandiose in scope is not to be made as such hypothesis cannot be easily put to test. A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used.

For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be explicated in terms of measurable indicator like income, wealth, etc. Such specificity in the formulation of a hypothesis ensures that the research is practicable and significant.

While framing the hypothesis, the researcher should be aware of the available research techniques and should see that the hypothesis framed is testable on the basis of them. In other words, a hypothesis should be researchable and for this it is important that a due thought has been given to the methods and techniques which can be used to measure the concepts and variables embodied in the hypothesis.

It does not however mean that hypotheses which are not testable with the available techniques of research are not to be made. If the problem is too significant and therefore the hypothesis framed becomes too ambitious and complex, it’s testing becomes possible with the development of new research techniques or the hypothesis itself leads to the development of new research techniques.

A hypothesis must be related to the existing theory or should have a theoretical orientation. The growth of knowledge takes place in the sequence of facts, hypothesis, theory and law or principles. It means the hypothesis should have a correspondence with the existing facts and theory.

If the hypothesis is related to some theory, the research work will enable us to support, modify or refute the existing theory. Theoretical orientation of the hypothesis ensures that it becomes scientifically useful. According to Prof. Goode and Prof. Hatt, research work can contribute to the existing knowledge only when the hypothesis is related with some theory.

This enables us to explain the observed facts and situations and also verify the framed hypothesis. In the words of Prof. Cohen and Prof. Nagel, “hypothesis must be formulated in such a manner that deduction can be made from it and that consequently a decision can be reached as to whether it does or does not explain the facts considered.”

If the research work based on a hypothesis is to be successful, it is necessary that the later is as simple and easy as possible. An ambition of finding out something new may lead the researcher to frame an unrealistic and unclear hypothesis. Such a temptation is to be avoided. Framing a simple, easy and testable hypothesis requires that the researcher is well acquainted with the related concepts.

Sources of Hypothesis

Hypotheses can be derived from various sources. Some of the sources is given below:

Observation

State of knowledge, continuity of research.

Hypotheses can be derived from observation from the observation of price behavior in a market. For example the relationship between the price and demand for an article is hypothesized.

Analogies are another source of useful hypotheses. Julian Huxley has pointed out that casual observations in nature or in the framework of another science may be a fertile source of hypotheses. For example, the hypotheses that similar human types or activities may be found in similar geophysical regions come from plant ecology.

This is one of the main sources of hypotheses. It gives direction to research by stating what is known logical deduction from theory lead to new hypotheses. For example, profit / wealth maximization is considered as the goal of private enterprises. From this assumption various hypotheses are derived’.

An important source of hypotheses is the state of knowledge in any particular science where formal theories exist hypotheses can be deduced. If the hypotheses are rejected theories are scarce hypotheses are generated from conception frameworks.

Another source of hypotheses is the culture on which the researcher was nurtured. Western culture has induced the emergence of sociology as an academic discipline over the past decade, a large part of the hypotheses on American society examined by researchers were connected with violence. This interest is related to the considerable increase in the level of violence in America.

The continuity of research in a field itself constitutes an important source of hypotheses. The rejection of some hypotheses leads to the formulation of new ones capable of explaining dependent variables in subsequent research on the same subject.

Null and Alternative Hypothesis

Null hypothesis.

The hypothesis that are proposed with the intent of receiving a rejection for them are called Null Hypothesis . This requires that we hypothesize the opposite of what is desired to be proved. For example, if we want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are not related.

Similarly, if we want to conclude that the new sales training programme is effective, we formulate the null hypothesis that the new training programme is not effective, and if we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.

Since we hypothesize that sales and advertisement are not related, new training programme is not effective and the average wages of skilled workers in both the towns are equal, we call such hypotheses null hypotheses and denote them as H 0 .

Alternative Hypothesis

Rejection of null hypotheses leads to the acceptance of alternative hypothesis . The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.

As already mentioned, the alternative hypotheses specify that values/relation which the researcher believes hold true. The alternative hypotheses can cover a whole range of values rather than a single point. The alternative hypotheses are denoted by H 1 .

Business Ethics

( Click on Topic to Read )

• What is Ethics?
• What is Business Ethics?
• Values, Norms, Beliefs and Standards in Business Ethics
• Indian Ethos in Management
• Ethical Issues in Marketing
• Ethical Issues in HRM
• Ethical Issues in IT
• Ethical Issues in Production and Operations Management
• Ethical Issues in Finance and Accounting
• What is Corporate Governance?
• What is Ownership Concentration?
• What is Ownership Composition?
• Types of Companies in India
• Internal Corporate Governance
• External Corporate Governance
• Corporate Governance in India
• What is Enterprise Risk Management (ERM)?
• What is Assessment of Risk?
• What is Risk Register?
• Risk Management Committee

Corporate social responsibility (CSR)

• Theories of CSR
• Arguments Against CSR
• Business Case for CSR
• Importance of CSR in India
• Drivers of Corporate Social Responsibility
• Developing a CSR Strategy
• Implement CSR Commitments
• CSR Marketplace
• CSR at Workplace
• Environmental CSR
• CSR with Communities and in Supply Chain
• Community Interventions
• CSR Monitoring
• CSR Reporting
• Voluntary Codes in CSR
• What is Corporate Ethics?

Lean Six Sigma

• What is Six Sigma?
• What is Lean Six Sigma?
• Value and Waste in Lean Six Sigma
• Six Sigma Team
• MAIC Six Sigma
• Six Sigma in Supply Chains
• What is Binomial, Poisson, Normal Distribution?
• What is Sigma Level?
• What is DMAIC in Six Sigma?
• What is DMADV in Six Sigma?
• Six Sigma Project Charter
• Project Decomposition in Six Sigma
• Critical to Quality (CTQ) Six Sigma
• Process Mapping Six Sigma
• Flowchart and SIPOC
• Gage Repeatability and Reproducibility
• Statistical Diagram
• Lean Techniques for Optimisation Flow
• Failure Modes and Effects Analysis (FMEA)
• What is Process Audits?
• Six Sigma Implementation at Ford
• IBM Uses Six Sigma to Drive Behaviour Change
• Research Methodology
• What is Research?
• Sampling Method
• Research Methods
• Data Collection in Research
• Methods of Collecting Data
• Application of Business Research
• Levels of Measurement
• What is Sampling?

Hypothesis Testing

• Research Report
• What is Management?
• Planning in Management
• Decision Making in Management
• What is Controlling?
• What is Coordination?
• What is Staffing?
• Organization Structure
• What is Departmentation?
• Span of Control
• What is Authority?
• Centralization vs Decentralization
• Organizing in Management
• Schools of Management Thought
• Classical Management Approach
• Is Management an Art or Science?
• Who is a Manager?

Operations Research

• What is Operations Research?
• Operation Research Models
• Linear Programming
• Linear Programming Graphic Solution
• Linear Programming Simplex Method
• Linear Programming Artificial Variable Technique
• Duality in Linear Programming
• Transportation Problem Initial Basic Feasible Solution
• Transportation Problem Finding Optimal Solution
• Project Network Analysis with Critical Path Method
• Project Network Analysis Methods
• Project Evaluation and Review Technique (PERT)
• Simulation in Operation Research
• Replacement Models in Operation Research

Operation Management

• What is Strategy?
• What is Operations Strategy?
• Operations Competitive Dimensions
• Operations Strategy Formulation Process
• What is Strategic Fit?
• Strategic Design Process
• Focused Operations Strategy
• Corporate Level Strategy
• Expansion Strategies
• Stability Strategies
• Retrenchment Strategies
• Competitive Advantage
• Strategic Choice and Strategic Alternatives
• What is Production Process?
• What is Process Technology?
• What is Process Improvement?
• Strategic Capacity Management
• Production and Logistics Strategy
• Taxonomy of Supply Chain Strategies
• Factors Considered in Supply Chain Planning
• Operational and Strategic Issues in Global Logistics
• Logistics Outsourcing Strategy
• What is Supply Chain Mapping?
• Supply Chain Process Restructuring
• Points of Differentiation
• Re-engineering Improvement in SCM
• What is Supply Chain Drivers?
• Supply Chain Operations Reference (SCOR) Model
• Customer Service and Cost Trade Off
• Internal and External Performance Measures
• Linking Supply Chain and Business Performance
• Netflix’s Niche Focused Strategy
• Disney and Pixar Merger
• Process Planning at Mcdonald’s

Service Operations Management

• What is Service?
• What is Service Operations Management?
• What is Service Design?
• Service Design Process
• Service Delivery
• What is Service Quality?
• Gap Model of Service Quality
• Juran Trilogy
• Service Performance Measurement
• Service Decoupling
• IT Service Operation
• Service Operations Management in Different Sector

Procurement Management

• What is Procurement Management?
• Procurement Negotiation
• Types of Requisition
• RFX in Procurement
• What is Purchasing Cycle?
• Vendor Managed Inventory
• Internal Conflict During Purchasing Operation
• Spend Analysis in Procurement
• Sourcing in Procurement
• Supplier Evaluation and Selection in Procurement
• Blacklisting of Suppliers in Procurement
• Total Cost of Ownership in Procurement
• Incoterms in Procurement
• Documents Used in International Procurement
• Transportation and Logistics Strategy
• What is Capital Equipment?
• Procurement Process of Capital Equipment
• Acquisition of Technology in Procurement
• What is E-Procurement?
• E-marketplace and Online Catalogues
• Fixed Price and Cost Reimbursement Contracts
• Contract Cancellation in Procurement
• Ethics in Procurement
• Legal Aspects of Procurement
• Global Sourcing in Procurement
• Intermediaries and Countertrade in Procurement

Strategic Management

• What is Strategic Management?
• What is Value Chain Analysis?
• Mission Statement
• Business Level Strategy
• What is SWOT Analysis?
• What is Competitive Advantage?
• What is Vision?
• What is Ansoff Matrix?
• Prahalad and Gary Hammel
• Strategic Management In Global Environment
• Competitor Analysis Framework
• Competitive Rivalry Analysis
• Competitive Dynamics
• What is Competitive Rivalry?
• Five Competitive Forces That Shape Strategy
• What is PESTLE Analysis?
• Fragmentation and Consolidation Of Industries
• What is Technology Life Cycle?
• What is Diversification Strategy?
• What is Corporate Restructuring Strategy?
• Resources and Capabilities of Organization
• Role of Leaders In Functional-Level Strategic Management
• Functional Structure In Functional Level Strategy Formulation
• Information And Control System
• What is Strategy Gap Analysis?
• Issues In Strategy Implementation
• Matrix Organizational Structure
• What is Strategic Management Process?

Supply Chain

• What is Supply Chain Management?
• Supply Chain Planning and Measuring Strategy Performance
• What is Warehousing?
• What is Packaging?
• What is Inventory Management?
• What is Material Handling?
• What is Order Picking?
• Receiving and Dispatch, Processes
• What is Warehouse Design?
• What is Warehousing Costs?

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The Scientific Method.

The scientific method is a process for creating models of the natural world that can be verified experimentally. The scientific method requires making observations, recording data, and analyzing data in a form that can be duplicated by other scientists. In addition, the scientific method uses inductive reasoning and deductive reasoning to try to produce useful and reliable models of nature and natural phenomena.

Inductive reasoning is the examination of specific instances to develop a general hypothesis or theory, whereas deductive reasoning is the use of a theory to explain specific results. In 1637 René Descartes published his Discours de la Méthode in which he described systematic rules for determining what is true, thereby establishing the principles of the scientific method.

The scientific method has four steps

• Observation and description of a phenomenon. The observations are made visually or with the aid of scientific equipment.
• Formulation of a hypothesis to explain the phenomenon in the form of a causal mechanism or a mathematical relation.
• Test the hypothesis by analyzing the results of observations or by predicting and observing the existence of new phenomena that follow from the hypothesis. If experiments do not confirm the hypothesis, the hypothesis must be rejected or modified (Go back to Step 2).
• Establish a theory based on repeated verification of the results.

The subject of a scientific experiment has to be observable and reproducible. Observations may be made with the unaided eye, a microscope, a telescope, a voltmeter, or any other apparatus suitable for detecting the desired phenomenon. The invention of the telescope in 1608 made it possible for Galileo to discover the moons of Jupiter two years later. Other scientists confirmed Galileo's observations and the course of astronomy was changed. However, some observations that were not able to withstand tests of objectivity were the canals of Mars reported by astronomer Percival Lowell. Lowell claimed to be able to see a network of canals in Mars that he attributed to intelligent life in that planet. Bigger telescopes and satellite missions to Mars failed to confirm the existence of canals. This was a case where the observations could not be independently verified or reproduced, and the hypothesis about intelligent life was unjustified by the observations. To Lowell's credit, he predicted the existence of the planet Pluto in 1905 based on perturbations in the orbits of Uranus and Neptune. This was a good example of deductive logic. The application of the theory of gravitation to the known planets predicted that they should be in a different position from where they were. If the law of gravitation was not wrong, then something else had to account for the variation. Pluto was discovered 25 years later.

Real science hops from failure to failure, from several falsifiable hypotheses in confused competition to the next set, until a consensus evolves around a surviving paradigm that often uses aspects of its predecessors, adding unexpected novel ideas that lead to productive questions and more definitive tests, as disparate data starts to fit an overall unifying view. — R. Murray

The apparatus for making a scientific observation has to be based on well-known scientific principles. The telescope, for instance, is based on magnification of an image using light refraction through lenses. It can be proved that the image perceived through the telescope corresponds to that of the object being observed. In other words, you can trust observations made through telescopes. This is in contrast to magic wands, divining rods, or other devices for which no basis in science can be found. A divining or dowsing rod is a "Y" shaped branch of a tree, which is supposed to be able to help to identify places where there is underground water. The operator holds the divining rod by the top of the "Y", and the single end is supposed to dip when the operator passes over a section of land where there is water. What is the force that makes the divining rod dip? How does the divining rod "sense" the water? A scientist would try to answer these questions by experiments. Place the divining rod on a scale, for example, and then put a bowl of water under the divining rod. Is there a change of weight that indicates force? In another experiment the scale with the divining rod may be placed over a place known to have underground water, and over another place known to be dry. If these experiments show no force being exerted on the divining rod, we have to conclude that divining rods cannot be used as instruments for detecting water. We also have to conclude that any movement of the rod is accomplished by the hands of the person holding it, no matter how much the person denies it.

The scientific method requires that theories be testable. If a theory cannot be tested, it cannot be a scientific theory. Testing of scientific ideas can include the classical experimental method, replication, attempted refutation, prediction, modeling, inference, deduction, induction and logical analysis. Step 2 involves inductive reasoning, as described above. This approach can be used to study gravitation, electricity, magnetism, optics, chemistry, etc. Sometimes more than one theory can be proposed to explain observable events. In such cases, different predictions made with each theory can be used to set up experiments that select one theory over another. In the 17th century there were competing theories about whether electromagnetic radiation, such as visible light, consisted of particles or waves. At the beginning of the 20th century Max Planck postulated that energy can only be emitted or absorbed in small, discrete packets called quanta. This seemed to favor the particle theory, particularly after Einstein demonstrated that light behaves like a stream of particles in photoelectric cells. However, diffraction experiments with electrons, which were considered particles because they had a measurable weight, showed all the characteristics of waves. In 1926, Erwin Schrödinger developed an equation that described the wave properties of matter, and this became the foundation for the branch of physics called quantum mechanics.

How can waves behave like particles and particles behave like waves? Some scientific facts are very hard to comprehend. Yet, these are observable phenomena verified over and over again by many people all over the world. The behavior of the speed of light is another physical fact that is hard to understand. The speed of light in a vacuum is approximately 299,792 kilometers per second. The speed is reduced by about 3% in air and by 25% in water. A famous experiment conducted by Michelson and Morely at the end of the 19th century showed that the speed of light was the same perpendicular to the orbit of the earth and parallel to the orbit of the earth. The orbital speed of the earth of 29 kilometers per second could not be detected in the measurement of the speed of light. Einstein's theory of relativity is based on the constancy of measurement of the speed of light for all observers. A train has its headlight on. The speed of the light emanating from the train is the same whether the train is moving toward you or not! It is hard to accept, but many experiments for over one hundred years have come to the same conclusion.

Limitations of the Scientific Method

Science has some well-known limitations. Science works by studying problems in isolation. This is very effective at getting good, approximate solutions. Problems outside these artificial boundaries are generally not addressed. The consistent, formal systems of symbols and mathematics used in science cannot prove all statements, and furthermore, they cannot prove all TRUE statements. Kurt Gödel showed this in 1931. The limitations of formal logical systems make it necessary for scientists to discard their old systems of thought and introduce new ones occasionally. Newton's gravitational model works fairly well for everyday physical descriptions, but it is not able to account for many important observations. For this reason, it has been replaced by Einstein's general theory of relativity for most celestial phenomena. Instead of talking about gravity, we now are supposed to talk about the curvature of the four-dimensional time-space continuum. Scientific observations are also subject to physical limits that may prevent us from finding the ultimate truth. The Heisenberg Uncertainty Principle states that it is impossible to determine simultaneously the position and momentum of an elementary particle. So, if we know the location of a particle we cannot determine its velocity, and if we know its velocity we cannot determine its location. Jacob Bronowski wrote that nature is not a gigantic formalizable system because to formalize it we would have to make some assumptions that cut some of its parts from consideration, and having done that, we cannot have a system that embraces the whole of nature.

The application of the scientific method is limited to independently observable, measurable events that can be reproduced. The scientific method is also applicable to random events that have statistical distributions. In atomic chemistry, for example, it is impossible to predict when one specific atom will decay and emit radiation, but it is possible to devise theories and formulas to predict when half of the atoms of a large sample will decay. Irreproducible results cannot be studied by the scientific method. There was one day when many car owners reported that the alarm systems of their cars were set off at about the same time without any apparent cause. Automotive engineers were not able to discover the reason because the problem could not be reproduced. They hypothesized that it could have been radio interference from a passing airplane, but they could not prove it one way or another. Mental conceptual experiences cannot be studied by the scientific method either. At this time there is no instrumentation that enables someone to monitor what anybody else conceives in their mind, although it is possible to determine which part of the brain is active during any given task. It is not possible to define experiments to determine objectively which works of art are "great", or whether Picasso was better than Matisse. So-called miracles are also beyond the scientific method. A person has tumors and faces certain death, and then, the tumors start shrinking and the person becomes healthy. What brought about the remission? A change in diet? A change in mental attitude? It is impossible to go back in time to monitor all variables that could have caused the cure, and it would be unethical to plant new tumors into the person to try to reproduce the results for a more careful study.

Critical Thinking

The scientific method relies on critical thinking, which is the process of questioning common beliefs and explanations to distinguish those beliefs that are reasonable and logical from those which lack adequate evidence or rational foundation.

Arguments consists of one or more premises and one conclusion. A premise is a statement that is offered in support of a claim being made. Premises and claims can be either true or false. In deductive arguments the premises provide complete support for the conclusion. If the premises provide the required degree of support for the conclusion then the argument is valid, and if all its premises are true, then the conclusion must be true. In inductive arguments the premises provide some degree of support for the conclusion. When the premises of inductive arguments are true, their conclusion is likely to be true. Arguments that have one or more false premises are unsound.

Arguments are subject to a variety of fallacies. A fallacy is an error in reasoning in which the premises given for the conclusion do not provide the needed degree of support. A deductive fallacy is a deductive argument where the premises are all true but reach a false conclusion. An inductive fallacy consist of arguments where the premises do not provide enough support for the conclusion. In such cases, even if the premises are true, the conclusion is not likely to be true.

Common fallacies are categorized by their type, such as Ad Hominem (personal attack), and appeals to authority, belief, fear, ridicule, tradition, etc. An example of an Ad Hominem fallacy would be to say "You do not understand this because you are American (or Chinese, etc.)". The national origin of a person (the premise) has nothing to do with the conclusion that a person can understand something or not, therefore the argument is flawed. Appeals to ridicule are of the form: "You would be stupid to believe that the earth goes around the sun". Sometimes, a naive or false justification may be added in appeals to ridicule, such as "we can plainly see the sun go around the earth every day". Appeals to authority are of the form "The president of the United States said this, therefore it must be true". The fact that a famous person, great person, or authority figure said something is not a valid basis for something being true. Truth is independent of who said it.

Types of Evidence

Direct or Experimental evidence. The scientific methods relies on direct evidence, i.e., evidence that can be directly observed and tested. Scientific experiments are designed to be repeated by other scientists and to demonstrate unequivocably the point that they are trying to prove by controlling all the factors that could influence the results. A scientist conducts an experiment by varying a single factor and observing the results.

When appropriate, "double blind" experiments are conducted to avoid the possibility of bias. If it is necessary to determine the effectiveness of a drug, an independent scientist will prepare the drug and an inert substance (a placebo), identifying them as A and B. A second scientist selects two groups of patients with similar characteristics (age, sex, etc.), and not knowing which is the real drug, administers substance A to one group of patients and substance B to the second group of patients. By not knowing whether A or B is the real drug, the second scientist focuses on the results of the experiment and can make objective evaluations. At the end of the experiment, the second scientist should be able to tell whether the group receiving substance A showed improvements over those receiving substance B. If no effect can be shown, the drug being tested is ineffective. Neither the second scientist nor the patients can cheat by favoring one substance over another, because they do not know which is the real drug.

Anecdotal, Correlational, or Circumstantial Evidence. "Where there is smoke, there is fire" is a popular saying. When two things occur together frequently, it is possible to assume that there is a direct or causative relationship between them, but it is also possible that there are other factors. For example, if you get sick every time that you eat fish and drink milk, you could assume that you are allergic to fish. However, you may be allergic to milk, or only to the combination of fish with milk. Correlational evidence is good for developing hypotheses that can then be tested with the proper experiments, e.g., drink milk only, eat fish only, eat fish and milk together.

There is nothing wrong with using representative cases to illustrate an inductive conclusion drawn from a fair sample. The problem arises when a single case or a few selected cases are used to draw a conclusion which would not be supported by a properly conducted study.

Argumentative Evidence consists of evaluating facts that are known and formulating a hypothesis about what the facts imply. Argumentative evidence is notoriously unreliable because anybody can postulate a hypothesis about anything. This was illustrated above with the example about the "channels" of Mars implying intelligent life. The statement "I heard a noise in the attic, it must be a ghost" also falls in this category.

Testimonial Evidence. A famous football player appears on television and says that Drug-XYZ provides relief from pain and works better than anything else. You know that the football player gets paid for making the commercial. How much can you trust this evidence? Not very much. Testimonials are often biased in favor of a particular point of view. In court proceedings, something actually experienced by a witness (eyewitness information) has greater weight than what someone told a witness (hearsay information). Nevertheless, experiments have repeatedly demonstrated that eyewitness accounts are highly unreliable when compared with films of the events. The statement "I saw a ghost last night." is an example of testimonial evidence that probably cannot be verified and should not be trusted. On the other hand, the statement "I saw a car crash yesterday." can be objectively verified to determine whether it is true or false by checking for debris from the accident, hospital records, and other physical evidence.

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The 5 Components of a Good Hypothesis

November 12, 2014 by Teresa Torres

Update: I’ve since revised this hypothesis format. You can find the most current version in this article:

• How to Improve Your Experiment Design (And Build Trust in Your Product Experiments)

“My hypothesis is …”

These words are becoming more common everyday. Product teams are starting to talk like scientists. Are you?

The internet industry is going through a mindset shift. Instead of assuming we have all the right answers, we are starting to acknowledge that building products is hard. We are accepting the reality that our ideas are going to fail more often than they are going to succeed.

Rather than waiting to find out which ideas are which after engineers build them, smart product teams are starting to integrate experimentation into their product discovery process. They are asking themselves, how can we test this idea before we invest in it?

This process starts with formulating a good hypothesis.

These Are Not the Hypotheses You Are Looking For

When we are new to hypothesis testing, we tend to start with hypotheses like these:

• Fixing the hard-to-use comment form will increase user engagement.
• A redesign will improve site usability.
• Reducing prices will make customers happy.

There’s only one problem. These aren’t testable hypotheses. They aren’t specific enough.

A good hypothesis can be clearly refuted or supported by an experiment. – Tweet This

To make sure that your hypotheses can be supported or refuted by an experiment, you will want to include each of these elements:

• the change that you are testing
• what impact we expect the change to have
• who you expect it to impact
• by how much
• after how long

The Change:  This is the change that you are introducing to your product. You are testing a new design, you are adding new copy to a landing page, or you are rolling out a new feature.

Be sure to get specific. Fixing a hard-to-use comment form is not specific enough. How will you fix it? Some solutions might work. Others might not. Each is a hypothesis in its own right.

Design changes can be particularly challenging. Your hypothesis should cover a specific design not the idea of a redesign.

In other words, use this:

• This specific design will increase conversions.
• Redesigning the landing page will increase conversions.

The former can be supported or refuted by an experiment. The latter can encompass dozens of design solutions, where some might work and others might not.

The Expected Impact:  The expected impact should clearly define what you expect to see as a result of making the change.

How will you know if your change is successful? Will it reduce response times, increase conversions, or grow your audience?

The expected impact needs to be specific and measurable. – Tweet This

You might hypothesize that your new design will increase usability. This isn’t specific enough.

You need to define how you will measure an increase in usability. Will it reduce the time to complete some action? Will it increase customer satisfaction? Will it reduce bounce rates?

There are dozens of ways that you might measure an increase in usability. In order for this to be a testable hypothesis, you need to define which metric you expect to be affected by this change.

Who Will Be Impacted: The third component of a good hypothesis is who will be impacted by this change. Too often, we assume everyone. But this is rarely the case.

I was recently working with a product manager who was testing a sign up form popup upon exiting a page.

I’m sure you’ve seen these before. You are reading a blog post and just as you are about to navigate away, you get a popup that asks, “Would you like to subscribe to our newsletter?”

She A/B tested this change by showing it to half of her population, leaving the rest as her control group. But there was a problem.

Some of her visitors were already subscribers. They don’t need to subscribe again. For this population, the answer to this popup will always be no.

Rather than testing with her whole population, she should be testing with just the people who are not currently subscribers.

This isn’t easy to do. And it might not sound like it’s worth the effort, but it’s the only way to get good results.

Suppose she has 100 visitors. Fifty see the popup and fifty don’t. If 45 of the people who see the popup are already subscribers and as a result they all say no, and of the five remaining visitors only 1 says yes, it’s going to look like her conversion rate is 1 out of 50, or 2%. However, if she limits her test to just the people who haven’t subscribed, her conversion rate is 1 out of 5, or 20%. This is a huge difference.

Who you test with is often the most important factor for getting clean results. – Tweet This

By how much: The fourth component builds on the expected impact. You need to define how much of an impact you expect your change to have.

For example, if you are hypothesizing that your change will increase conversion rates, then you need to estimate by how much, as in the change will increase conversion rate from x% to y%, where x is your current conversion rate and y is your expected conversion rate after making the change.

This can be hard to do and is often a guess. However, you still want to do it. It serves two purposes.

First, it helps you draw a line in the sand. This number should determine in black and white terms whether or not your hypothesis passes or fails and should dictate how you act on the results.

Suppose you hypothesize that the change will improve conversion rates by 10%, then if your change results in a 9% increase, your hypothesis fails.

This might seem extreme, but it’s a critical step in making sure that you don’t succumb to your own biases down the road.

It’s very easy after the fact to determine that 9% is good enough. Or that 2% is good enough. Or that -2% is okay, because you like the change. Without a line in the sand, you are setting yourself up to ignore your data.

The second reason why you need to define by how much is so that you can calculate for how long to run your test.

After how long:  Too many teams run their tests for an arbitrary amount of time or stop the results when one version is winning.

This is a problem. It opens you up to false positives and releasing changes that don’t actually have an impact.

If you hypothesize the expected impact ahead of time than you can use a duration calculator to determine for how long to run the test.

Finally, you want to add the duration of the test to your hypothesis. This will help to ensure that everyone knows that your results aren’t valid until the duration has passed.

If your traffic is sporadic, “how long” doesn’t have to be defined in time. It can also be defined in page views or sign ups or after a specific number of any event.

Putting It All Together

Use the following examples as templates for your own hypotheses:

• Design x [the change] will increase conversions [the impact] for search campaign traffic [the who] by 10% [the how much] after 7 days [the how long].
• Reducing the sign up steps from 3 to 1 will increase signs up by 25% for new visitors after 1,000 visits to the sign up page.
• This subject line will increase open rates for daily digest subscribers by 15% after 3 days.

After you write a hypothesis, break it down into its five components to make sure that you haven’t forgotten anything.

• Change: this subject line
• Impact: will increase open rates
• Who: for daily digest subscribers
• By how much: by 15%
• After how long: After 3 days

And then ask yourself:

• Is your expected impact specific and measurable?
• Can you clearly explain why the change will drive the expected impact?
• Are you testing with the right population?
• Did you estimate your how much based on a baseline and / or comparable changes? (more on this in a future post)
• Did you calculate the duration using a duration calculator?

It’s easy to give lip service to experimentation and hypothesis testing. But if you want to get the most out of your efforts, make sure you are starting with a good hypothesis.

Did you learn something new reading this article? Keep learning. Subscribe to the Product Talk mailing list to get the next article in this series delivered to your inbox.

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May 21, 2017 at 2:11 am

Interesting article, I am thinking about making forming a hypothesis around my product, if certain customers will find a proposed value useful. Can you kindly let me know if I’m on the right track.

“Certain customer segment (AAA) will find value in feature (XXX), to tackle their pain point ”

Change: using a feature (XXX)/ product Impact: will reduce monetary costs/ help solve a problem Who: for certain customers segment (AAA) By how much: by 5% After how long: 10 days

April 4, 2020 at 12:33 pm

Hi! Could you throw a little light on this: “Suppose you hypothesize that the change will improve conversion rates by 10%, then if your change results in a 9% increase, your hypothesis fails.”

I understood the rationale behind having a number x (10% in this case) associated with “by how much”, but could you explain with an example of how to ballpark a figure like this?

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What are the parts of a hypothesis?

A hypothesis should: (1) be written in a declarative sentence; (2) be written in present tense; (3) contain the population; (4) contain the variables; (5) reflect the problem or purpose statement; and (6) be empirically testable.

Add your answer:

What are the parts of scientific investigation?

The parts of scientific investigation are identifying a research question or problem, forming a hypothesis, gathering evidence, analyzing evidence.

How are hypothesis and conclusion related?

you first start off with a hypothesis and after you end up with a conclusion

What are the five parts of scientific investigation?

1. Have a question. 2. Make a Hypothesis. 3. Test the Hypothesis. 4. Collect and analyze data. 5. Come to a conclusion where you state whether or not your hypothesis was correct.

What are 4 main parts of the scientific method?

observation,hypothesis, experiment, and thesis/conclusion

What are the 7 parts of the scientific method in order?

problem,observation,inference,hypothesis,experiment,conclusion,result

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• High School

What are the 4 parts of a hypothesis?

Junebug6589 is waiting for your help., expert-verified answer.

• 2.3K answers
• 952.8K people helped

The hypothesis is generally for the test of several plots and summarises or for the assertion and reasonable questions as if in an experiment also hyp o t hesis are used frequently.

The not unusualplace steps in all 3 techniques of speculation trying out is the primary step, that is to kingdom the null and opportunity speculation. The 2d step of the check statistic technique is to decide the check length and to gain the crucial value .

The 1/3 step is to compute the check statistic. The Null and Alternative Hypotheses. The Test Statistic. Probability Values and Statistical Significance. The Conclusions of Hypothesis Testing.

Read more about hypothesis :

brainly.com/question/11555274

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Get more answers for free, you might be interested in, new questions in biology.

UK riots latest: Dawn raids launched to catch riot suspects - as warning more 'events' planned

Anti-racism protesters turned out in their thousands across the country last night as 100 anticipated far right protests largely failed to materialise. More rioters who took part in violent disorder across UK towns and cities over the past week will be sentenced today.

Thursday 8 August 2024 07:48, UK

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• Thousands of counter-protesters hit streets across England as 'nightmare' of 100 far-right demonstrations fails to materialise
• Police boss: Show of force and unity won out - as dawn raids launched
• Rioters to face punishment in courts
• In pictures: Signs of 'love', 'unity', and 'good vibes'
• Watch: Pubgoers embrace Muslims 'defending their community'
• Live reporting by Brad Young
• Listen: 'Unease and fear' - How Muslim communities feel about the riots
• Watch : Bodycam footage of Southport disorder
• What does 'two-tier' policing mean - and does it exist?
• Analysis: Fast-track sentencing designed to be 'deterrent' to rioters
• Data: Far-right social media activity outnumbers anti-racist movement

Police forces have released CCTV images in an appeal to identify people in connection with the far-right violence over the past week.

Merseyside Police issued pictures of 14 people, while South Yorkshire police have published 21 pictures in connection with violent disorder outside the Holiday Inn Express in Rotherham on Sunday. The hotel has been housing asylum seekers.

Detective Superintendent Paul Speight of Merseyside Police said: "It is vital that these people make themselves known as soon as possible, as we think they can assist our ongoing investigations.

"We are still working our way through information, images and footage as it comes in, and we'll continue to take action to arrest, charge and put before the courts anyone identified."

You can see all 35 images in the story below:

While Met police chief Sir Mark Rowley praised last night as a success, policing minister Dame Diana Johnson says she is feeling "cautious".

"It's good that we didn't see the level of disorder and criminality on our streets that we have in previous days," she told Sky News, "but obviously this is just the start."

"There is now further intelligence of events during the next few days, and we need to see what happens there."

A show of force and a show of unity won out last night, the head of the Met Police has said.

Fears of extreme riots were abated by police and communities, as counter-protests took place "very peacefully", Sir Mark Rowley said.

"Dawn raids" were carried out this morning to arrest suspected violent offenders from recent days, he added.

On yesterday's operation, Sir Mark said: "I'm really pleased with how it went. We put thousands of officers on the street and I think the show of force from the police and, frankly, the show of unity from communities together defeated the challenges that we've seen."

Police continue to work through footage and evidence gathered over the last week of riots and will be sweeping up offenders over the next 24 hours, he said.

"These are criminal thugs, any suggestion that they are patriots or have got a cause that they are protesting about is nonsense.

"They are criminals and frankly most of them will be charged with violent disorder and most are going to go to prison for a few years."

About 70% of the most violent offenders have criminal backgrounds, he said.

Rioters who have admitted violent disorder in the past week are due to be sentenced today.

Home affairs reporter Henry Vaughan will be reporting from Liverpool Crown Court, with more cases concluding at Plymouth Crown Court.

Other suspects are appearing in court to face charges, including at Hull Magistrates' Court.

Police have made more than 400 arrests over riots fuelled by the killing of three girls in a stabbing attack in Southport last Monday, and are considering using counter-terrorism laws to prosecute some rioters. 

The government has pledged to prosecute those responsible for the disorder, including those who use social media to incite the violence. 

Police had prepared for the biggest night of violence since the Southport attacks last night, with far-right protests planned at 100 locations after a week of rioting and disorder.

Many businesses had boarded up windows and closed down in fear of what lay ahead.

Stand Up To Racism and other groups had planned counter-protests in response - and in most places they reclaimed their streets with nothing to oppose.

A crowd of immigrant supporters that quickly grew to several hundred in the London neighbourhood of North Finchley found themselves largely alone with several dozen police officers.

The crowd chanted "refugees welcome" and "London against racism". 

Some held signs saying "Stop the far right," "Migration is not a crime" and "Finchley against Fascism".

At one point, a man who had been shouting at the group and pulling his shirt up to show off an eagle tattoo was punched by a protester.

Outside an immigration centre in the Walthamstow area in east London, an anti-racism protest leader shouted "fascist scum" to which a crowd of hundreds responded: "Off our streets."

In Liverpool , hundreds showed up to defend the Asylum Link immigration centre. 

A grandmother held a placard reading "Nans Against Nazis" and someone else held a sign saying: "When the poor blame the poor only the rich win."

In Birmingham , several hundred anti-racism protesters - some carrying signs such as "no place for hate" and "bigots out of Brum" - gathered outside a migrant centre in the Jewellery Quarter.

A large group then marched into the centre of the city, with no signs of any far-right groups in the area.

Several thousand also turned out in Bristol , said our correspondent Dan Whitehead - it was a calm and relaxed atmosphere with some people playing music.

Crime correspondent Martin Brunt said there were reports of "small pockets" of far-right protesters in places such as Portsmouth, Brighton and Blackpool.

But worries it could be the biggest night of trouble so far faded away.

Earlier intelligence suggesting 100 far-right protests had been "credible", he said, adding that police would now be analysing how they managed to avoid that "nightmare" scenario.

Welcome back to our live coverage of unrest in the UK, after far-right rallies largely failed to materialise last night.

Instead, police were met with thousands of peaceful anti-racist counter-protesters.

In London, Bristol, Oxford, Liverpool and Birmingham, large crowds gathered outside agencies and law firms specialising in immigration that had been listed by internet chat groups as possible targets of far-right activity.

In resounding choruses they chanted: "Whose streets? Our streets!" subverting a typically far-right refrain.

In addition to thousands of officers already deployed, about  1,300 specialist forces were on standby in case of serious trouble in London.

But with the exception of scattered disturbances and some arrests, riots did not erupt.

Before we resume our live coverage, here is a quick recap of some of the other key moments that occurred over the last day.

• There were more arrests in relation to the far-right violence, as well as some anti-social behaviour in Croydon, south London;
• A rioter who punched a police officer in face was jailed for three years;
• One case of alleged terrorism was "actively under consideration", director of public prosecutions Stephen Parkinson has said;

That's all our coverage on this story for now - we'll be back tomorrow with all the latest updates. 

Before we go, here's a reminder of the key events from the past 24 hours: 

• Thousands of counter-protesters have taken to the streets across England in response to days of unrest from far-right rioters;
• The 100 far-right demonstrations that police were bracing for largely failed to materialise;
• There have been more arrests this evening in relation to the far-right violence, as well as some anti-social behaviour in Croydon, south London;
• More of the rioters were convicted earlier today, including one man who punched a police officer in the face, who was jailed for three years; 
• One case is actively being investigated as terrorism, the director of public prosecutions revealed.

Tonight's counter demonstrations have made the front page of several of the newspapers' Thursday editions, after several days of coverage of the far-right riots.

Here is a selection.

The i reported that rioters involved in the unrest in recent days may face being banned from attending football matches.

Meanwhile, Metro reports that supporters of Vladimir Putin have infiltrated the online chat groups being used to organise the far-right gatherings.

More from the Met Police this evening. 

The force said they made 15 arrests around the capital - 10 in Croydon, four in Waltham Forest and one in Hounslow. 

The anti-racist protests in Walthamstow and Finchley "passed without major incident", the Met said, but they faced "anti-social behaviour from a small group in Croydon who were not related to any protest, but were intent on causing trouble".

In Croydon, four people were arrested on suspicion of breaching the dispersal order, four more on suspicion of violent disorder, one on suspicion of an assault on an emergency worker, and one on suspicion for going equipped for arson. 

In Waltham Forest, two were arrested on suspicion of having an offensive weapon - which police said was a lock knife and golf club - one on suspicion of being drunk and disorderly, and one on suspicion of obstructing a stop and search. 

The arrest in Hounslow was in relation to possession of an offensive weapon.

Deputy Assistant Commissioner Andy Valentine said: "This evening more than 1,000 officers were deployed in London ready to respond to events planned across the city. They were ready to protect our communities from hateful, divisive and violent behaviour.

"Our top priority has been to keep our communities safe, prevent significant disorder and swiftly deal with any offenders. A number of arrests were made across the city this evening. 

"I want to thank our communities for coming together across the capital and for showing community spirit this evening," he added.

"I would also like to thank the committed officers who have been working hard to police London today, serving their communities and keeping people safe."

By Henry Vaughan , home affairs reporter

Liverpool Crown Court earlier heard details of the impact rioting has had on police and the community.

The chief constable of Merseyside Police wrote a witness statement for the sentencing hearing of three men involved in the disorder. 

Serena Kennedy said more than 150 officers from her force, and between 75 and 150 from others, had been deployed every day since 31 July to respond to disorder.

A team of 55 staff is investigating the disorder along with specialist investigators looking into online incidents, which means "taking staff away from investigating other crimes", she said, while the cost to policing "is immense" given the existing £21m budget shortfall.

As we reported earlier, 93 officers were injured in incidents in Merseyside on the nights of 30 July and 2 and 3 August, with injuries including leg fractures, a broken jaw and teeth knocked out. Police dogs have also been injured.

"The level of aggression I witnessed which was directly aimed at my officers… is unprecedented," she said.

"It was horrifying to see."

'Panic attacks'

The chief constable said many of those who have been attacked talked of "psychological symptoms" along with their physical injuries.

"Some have been waking up in the night with panic attacks while others have described the fear they felt whilst deployed to the disorder that they may not return home safely to their families.

"A number of victims have expressed their disbelief that officers have not been killed as a result of the appalling scenes of violence they have faced."

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