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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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On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

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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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• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

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Step 1: ask a question.

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

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

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

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

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

A hypothesis is a tentative statement about the relationship between two or more  variables. It is a specific, testable prediction about what you expect to happen in a study.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

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

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

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

To form a hypothesis, you should take these steps:

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

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

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

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

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

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

These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable.   By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

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

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• Complex hypothesis: "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

A Word From Verywell

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

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

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

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

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Meaning of hypothesis in English

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• abstraction
• afterthought
• anthropocentrism
• anti-Darwinian
• exceptionalism
• foundation stone
• great minds think alike idiom
• non-dogmatic
• non-empirical
• non-material
• non-practical
• social Darwinism
• supersensible
• the domino theory

hypothesis | American Dictionary

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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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 ).

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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Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.

But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including: 

• Defining the term “hypothesis” 
• Providing hypothesis examples 
• Giving you tips for how to write your own hypothesis

So let’s get started!

What Is a Hypothesis?

Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid. 

As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.

Hypotheses are one part of what’s called the scientific method .  Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):

• Observation
• Asking questions
• Forming a hypothesis
• Analyze the data
• Communicate your results

You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!

Independent and Dependent Variables 

After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.

There are two types of variables:  independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable. 

Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets. 

Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.

Elements of a Good Hypothesis

The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.

As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.

Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.

Writing Your Hypothesis

Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.

When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.

The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement! 

In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.

Additionally, keep in mind that hypotheses can range from very specific to very broad.  These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.  

The Two Types of Hypotheses

Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.

#1: If-Then Hypotheses

First of all, if-then hypotheses typically follow this formula:

If ____ happens, then ____ will happen.

The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life: 

• If I get enough sleep, I’ll be able to get more work done tomorrow.
• If the bus is on time, I can make it to my friend’s birthday party. 
• If I study every night this week, I’ll get a better grade on my exam. 

In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades). 

You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:

“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”

It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.

#2: Null Hypotheses

Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .

One null hypothesis for the cell phone and sleep study from the last section might say: 

“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.” 

In this case, this is a null hypothesis because it’s asking the opposite of the original thesis! 

Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:

“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”

In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:

“If people have many followers on Instagram, they will spend more time on the app than people who have less.”

You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship. 

4 Tips to Write the Best Hypothesis

If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.

#1: Plausibility

At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think. 

Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than  twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.

Improbable hypotheses generally go against  science, as well. Take this hypothesis example: 

“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.” 

This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.

#2: Defined Concepts

The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.

Here’s what we mean. Which of the following sentences makes more sense to the common person?

If the kerning is greater than average, more words will be read per minute.

If the space between letters is greater than average, more words will be read per minute.

For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible. 

Good hypotheses ensure that you can observe the results. 

#3: Observability

In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.

Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable. 

In writing your hypothesis, always keep in mind how you'll execute the experiment.

#4: Generalizability 

Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.

Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?

Hypothesis Testing Examples

We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.

Experiment #1: Students Studying Outside (Writing a Hypothesis)

You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?

You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:

If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”

Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”

These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.

To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come  and how many leave. You also write down the temperature on the hour. 

After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.

Experiment #2: The Cupcake Store (Forming a Simple Experiment)

Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?

Here’s what your hypotheses might look like: 

If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”

Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”

This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).

Experiment #3: Backyard Bird Feeders (Integrating Multiple Variables and Rejecting the If-Then Hypothesis)

While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment. 

However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this: 

If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”

Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”

Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.

Experiment #4: In-Class Survey (Including an Alternative Hypothesis)

You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while: 

If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.

Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.

You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?

This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again! 

Key Takeaways: Hypothesis Writing

The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.

Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.

What’s Next?

If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)

If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.

If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home

Ashley Sufflé Robinson has a Ph.D. in 19th Century English Literature. As a content writer for PrepScholar, Ashley is passionate about giving college-bound students the in-depth information they need to get into the school of their dreams.

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Definition of a Hypothesis

What it is and how it's used in sociology

• Key Concepts
• Major Sociologists
• News & Issues
• Research, Samples, and Statistics
• Recommended Reading
• Archaeology

A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.

In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.

Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true. 

Null Hypothesis

A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.

Alternative Hypothesis

Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.

Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.

Formulating a Hypothesis

Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.

Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

Updated by Nicki Lisa Cole, Ph.D

• What Is a Hypothesis? (Science)
• Understanding Path Analysis
• Null Hypothesis Examples
• What Are the Elements of a Good Hypothesis?
• What 'Fail to Reject' Means in a Hypothesis Test
• How Intervening Variables Work in Sociology
• Null Hypothesis Definition and Examples
• Understanding Simple vs Controlled Experiments
• Scientific Method Vocabulary Terms
• Null Hypothesis and Alternative Hypothesis
• Six Steps of the Scientific Method
• What Are Examples of a Hypothesis?
• Structural Equation Modeling
• Scientific Method Flow Chart
• How To Design a Science Fair Experiment
• Hypothesis Test for the Difference of Two Population Proportions

What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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This is the Difference Between a Hypothesis and a Theory

What to Know A hypothesis is an assumption made before any research has been done. It is formed so that it can be tested to see if it might be true. A theory is a principle formed to explain the things already shown in data. Because of the rigors of experiment and control, it is much more likely that a theory will be true than a hypothesis.

As anyone who has worked in a laboratory or out in the field can tell you, science is about process: that of observing, making inferences about those observations, and then performing tests to see if the truth value of those inferences holds up. The scientific method is designed to be a rigorous procedure for acquiring knowledge about the world around us.

In scientific reasoning, a hypothesis is constructed before any applicable research has been done. A theory, on the other hand, is supported by evidence: it's a principle formed as an attempt to explain things that have already been substantiated by data.

Toward that end, science employs a particular vocabulary for describing how ideas are proposed, tested, and supported or disproven. And that's where we see the difference between a hypothesis and a theory .

A hypothesis is an assumption, something proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

What is a Hypothesis?

A hypothesis is usually tentative, an assumption or suggestion made strictly for the objective of being tested.

When a character which has been lost in a breed, reappears after a great number of generations, the most probable hypothesis is, not that the offspring suddenly takes after an ancestor some hundred generations distant, but that in each successive generation there has been a tendency to reproduce the character in question, which at last, under unknown favourable conditions, gains an ascendancy. Charles Darwin, On the Origin of Species , 1859 According to one widely reported hypothesis , cell-phone transmissions were disrupting the bees' navigational abilities. (Few experts took the cell-phone conjecture seriously; as one scientist said to me, "If that were the case, Dave Hackenberg's hives would have been dead a long time ago.") Elizabeth Kolbert, The New Yorker , 6 Aug. 2007

What is a Theory?

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, its likelihood as truth is much higher than that of a hypothesis.

It is evident, on our theory , that coasts merely fringed by reefs cannot have subsided to any perceptible amount; and therefore they must, since the growth of their corals, either have remained stationary or have been upheaved. Now, it is remarkable how generally it can be shown, by the presence of upraised organic remains, that the fringed islands have been elevated: and so far, this is indirect evidence in favour of our theory . Charles Darwin, The Voyage of the Beagle , 1839 An example of a fundamental principle in physics, first proposed by Galileo in 1632 and extended by Einstein in 1905, is the following: All observers traveling at constant velocity relative to one another, should witness identical laws of nature. From this principle, Einstein derived his theory of special relativity. Alan Lightman, Harper's , December 2011

Non-Scientific Use

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch (though theory is more common in this regard):

The theory of the teacher with all these immigrant kids was that if you spoke English loudly enough they would eventually understand. E. L. Doctorow, Loon Lake , 1979 Chicago is famous for asking questions for which there can be no boilerplate answers. Example: given the probability that the federal tax code, nondairy creamer, Dennis Rodman and the art of mime all came from outer space, name something else that has extraterrestrial origins and defend your hypothesis . John McCormick, Newsweek , 5 Apr. 1999 In his mind's eye, Miller saw his case suddenly taking form: Richard Bailey had Helen Brach killed because she was threatening to sue him over the horses she had purchased. It was, he realized, only a theory , but it was one he felt certain he could, in time, prove. Full of urgency, a man with a mission now that he had a hypothesis to guide him, he issued new orders to his troops: Find out everything you can about Richard Bailey and his crowd. Howard Blum, Vanity Fair , January 1995

And sometimes one term is used as a genus, or a means for defining the other:

Laplace's popular version of his astronomy, the Système du monde , was famous for introducing what came to be known as the nebular hypothesis , the theory that the solar system was formed by the condensation, through gradual cooling, of the gaseous atmosphere (the nebulae) surrounding the sun. Louis Menand, The Metaphysical Club , 2001 Researchers use this information to support the gateway drug theory — the hypothesis that using one intoxicating substance leads to future use of another. Jordy Byrd, The Pacific Northwest Inlander , 6 May 2015 Fox, the business and economics columnist for Time magazine, tells the story of the professors who enabled those abuses under the banner of the financial theory known as the efficient market hypothesis . Paul Krugman, The New York Times Book Review , 9 Aug. 2009

Incorrect Interpretations of "Theory"

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general use to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

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What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

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

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables.

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How to Develop a Good Research Hypothesis

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

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

Table of Contents

What is Hypothesis?

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

What is a Research Hypothesis?

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

Characteristics of a Good Research Hypothesis

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

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

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

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

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

Source: Educational Hub

How to formulate a research hypothesis.

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

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

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

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

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

3. Define the variables

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

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

4. Scrutinize the hypothesis

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

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

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

2. Complex Hypothesis

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

3. Directional Hypothesis

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

4. Non-directional Hypothesis

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

5. Associative and Causal Hypothesis

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

6. Null Hypothesis

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

7. Alternative Hypothesis

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

Research Hypothesis Examples of Independent and Dependent Variables

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

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

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

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

Importance of a Testable Hypothesis

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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

Thanks a lot for your valuable guidance.

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

Useful piece!

This is awesome.Wow.

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

Nicely explained

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

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

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

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

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

What is Hypothesis?

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

Hypothesis Meaning

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

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

Characteristics of Hypothesis

Here are some key characteristics of a hypothesis:

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

Sources of Hypothesis

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

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

Types of Hypothesis

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

• Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

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

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

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

Non-Directional Hypothesis

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

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

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

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

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

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

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

Hypothesis Examples

Following are the examples of hypotheses based on their types:

Simple Hypothesis Example

• Studying more can help you do better on tests.
• Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis Example

• How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
• A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis Example

• Drinking more sweet drinks is linked to a higher body weight score.
• Too much stress makes people less productive at work.

Non-directional Hypothesis Example

• Drinking caffeine can affect how well you sleep.
• People often like different kinds of music based on their gender.
• The average test scores of Group A and Group B are not much different.
• There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis (Ha)

• Patients on Diet A have much different cholesterol levels than those following Diet B.
• Exposure to a certain type of light can change how plants grow compared to normal sunlight.
• The average smarts score of kids in a certain school area is 100.
• The usual time it takes to finish a job using Method A is the same as with Method B.
• Having more kids go to early learning classes helps them do better in school when they get older.
• Using specific ways of talking affects how much customers get involved in marketing activities.
• Regular exercise helps to lower the chances of heart disease.
• Going to school more can help people make more money.
• Playing violent video games makes teens more likely to act aggressively.
• Less clean air directly impacts breathing health in city populations.

Functions of Hypothesis

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

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

How Hypothesis help in Scientific Research?

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

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

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations. The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology. The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data, ultimately driving scientific progress through a cycle of testing, validation, and refinement.

FAQs on Hypothesis

What is a hypothesis.

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

What are Components of a Hypothesis?

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

What makes a Good Hypothesis?

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

Can a Hypothesis be Proven True?

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

How are Hypotheses Tested?

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

Can Hypotheses change during Research?

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

What is the Role of a Hypothesis in Scientific Research?

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

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Empirical studies of the “similarity leads to attraction” hypothesis in workplace interactions: a systematic review

• Open access
• Published: 16 January 2023

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• Zoleikha Abbasi   ORCID: orcid.org/0000-0002-8803-2239 1 ,
• Jon Billsberry   ORCID: orcid.org/0000-0002-3015-4196 1 &
• Mathew Todres   ORCID: orcid.org/0000-0001-6496-7809 1  

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Although the similarity-attraction hypothesis (SAH) is one of the main theoretical foundations of management and industrial/organizational (I/O) psychology research, systematic reviews of the hypothesis have not been published. An overall review of the existing body of knowledge is therefore warranted as a means of identifying what is known about the hypothesis and also identifying what future studies should investigate. The current study focuses on empirical workplace SAH studies. This systematic review surfaced and analyzed 49 studies located in 45 papers. The results demonstrate that SAH is valid in organizational settings and it is a fundamental force driving employees’ behavior. However, the force is not so strong that it cannot be overridden or moderated by other forces, which includes forces from psychological, organizational, and legal domains. This systematic review highlights a number of methodological issues in tests of SAH relating to the low number of longitudinal studies, which is important given the predictive nature of the hypotheses, and the varying conceptualizations of attraction measurement.

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1 Introduction

Individuals are positively inclined towards people who are similar to themselves. This simple but striking assertion underpins the similarity-attraction hypothesis (SAH), which frames much relationship and interpersonal attraction research (e.g., Byrne 1971 ; Montoya and Horton 2013 ). According to Byrne ( 1971 ), when people perceive themselves to be similar to other people, they experience positive feelings of attraction towards them. These similarities cover a large number of factors typically separated into demographic (e.g., race, gender, ethnicity, socio-economic background, and age) and psychological (e.g., personality, values, interests, religion, education, and occupation) divisions. Many studies have shown that similarities in these various forms lead to friendships and other close relationships (e.g., Graziano and Bruce 2008 ; Kleinbaum et al. 2013 ; McPherson et al. 2001 ; Riordan 2000 ).

In work settings, SAH is a double-edged sword. On the one hand, it is a fundamental human drive that underpins effective social interaction in workplaces (e.g., McPherson et al. 2001 ; Montoya and Horton 2012 ), but on the other hand, it can lead to affinity or similarity bias and exclude those unlike the people making decisions (e.g., Björklund et al. 2012 ; Coates and Carr 2005 ; Hambrick 2007 ; O’Reilly et al. 2014 ; Sacco et al. 2003 ). To combat such ‘natural’ biases, most countries have passed laws to protect employees and potential employees who are dissimilar to those currently employed by organizations and who wield considerable power to decide who can enter organizations, who gets promoted, and how people are treated at work. Hence, within organizational settings, there is an eternal conflict at the heart of this field study; a conflict between natural human processes and natural justice.

In the mid 1990s, two reviews of work-related SAH appeared. An unpublished paper by Alliger et al. ( 1993 ) reviewed SAH in the context of personnel selection decision and work relations. Pierce et al. ( 1996 ) reviewed the hypothesis through the lens of romance in the workplace. There do not appear to be any more recent reviews and no broad-sweep reviews of SAH in the workplace. This paper makes a contribution by conducting such a study with the goal of reviewing extant knowledge on SAH in the workplace. Prior to a detailed account of the methods used for our systematic review, we first contextualize the current study by discussing relevant concepts and their evolution over time, namely the SAH and attraction, respectively. In our findings section, we review the cluster of studies providing empirical support for SAH, draw attention to measurement-design issues, and look at the study of SAH during the distinct organizational phases of recruitment and selection, employment, and organizational exit. In the discussion, we further the contribution of the paper with an examination of the paradox of similarity effects in an age when diversity and inclusion are prime considerations for organizations. We also draw out methodological challenges in the extant SAH in the workplace literature.

2 Similarity-attraction hypothesis

Although scientific focus on SAH gathered steam in the 1950s and 1960s (e.g., Byrne 1961 ; Festinger et al. 1950 ; Newcomb 1961 ; Walster et al. 1966 ), it has been studied for much longer. The relation between similarity and interpersonal attraction was mentioned as early as 1870 by Sir Francis Galton, who observed that illustrious men married illustrious women. Terman ( 1938 ) demonstrated that the greater the similarity between husband and wife, the more successful the marriage.

In the 1950s and 1960s, SAH studies focused on the interpersonal space and the role of attribution in attraction. In one of the first examples, Newcomb ( 1961 ) analyzed the establishment of friendships between new students at a college residence. He recorded students’ demographic information, attitudes, values, and beliefs and then measured their interpersonal attraction to each other. He showed that similarity between the students was the main predictor of attraction amongst them. Taking an experimental approach, Byrne and his colleagues used a “bogus stranger” methodology, in which they varied the similarity of a perceiver’s attitudes to those of a stranger, and then quantified the liking of that stranger. In a series of studies (e.g., Byrne 1971 , 1997 ; Byrne and Blaylock 1963 ; Byrne and Clore 1967 ; Byrne and Nelson 1965 ), they demonstrated that attitude similarity delivered more liking of the target.

Byrne and Clore ( 1967 ) presented a reinforcement model to explain the positive relationship between similarity and attraction, in which similarity presents social validation of one’s views of the self and the world, thus helping to satisfy an individual’s needs. The positive influence that is caused by this need fulfillment becomes correlated with its source, namely the similar individual, and leads to their liking. As an alternative, dissimilarity jeopardizes epistemic needs (i.e., the desire for establishing understanding) as it challenges one’s views about the self and the world, and therefore stimulates negative affect that, in turn, becomes linked with the dissimilar person. Consequently, according to Byrne and Clore ( 1967 ), personal attraction is a conditioned reaction to the positive or negative effect that is created by an unconditioned similarity motivation.

While cognition was recognized as an essential aspect of shaping a judgment of another, the data indicated that the more attitudes individuals held in common with each other, the more attracted they were to the other person (Byrne 1971 , 1997 ). This explains why people are attracted to like-minded individuals. Motivation to find others who are similar may have something to do with keeping a person’s perspective coherent with what they already know; people struggle for guaranteed certainty in dealing with the world around them (Byrne et al. 1966 ). Drawing on Newcomb ( 1956 ), Byrne ( 1997 ) argues that a key reason explaining the repeated support for SAH is due to the interpersonal rewards that follow from attraction: “At its simplest level, […] people like feeling good and dislike feeling bad” (Byrne 1997 : 425). Further, assessments of similarity increase the validation of individuals’ values, which leads to attraction, harmony, and cooperation between individuals (Edwards and Cable 2009 ). Thus, the more similar people perceive themselves to be to each other, the more attractive they will be to each other.

Although most SAH studies have researched similarities in peoples’ attitudes, concluding that individuals are more attracted to people with whom they have many shared attitudes (Byrne 1961 ; Byrne et al. 1970 ; Kaptein et al. 2014 ), studies have found that actual similarity in external characteristics (e.g., age, hairstyle) is more predictive of attraction than similarity in psychological characteristics such as cleverness and confidence (Condon and Crano 1988 ; Duck and Craig 1975 ; Montoya et al. 2008 ). Amongst the demographic attributes most commonly studied are age, education, ethnic background, religious affiliation (Gardiner 2022 ; Grigoryan 2020 ), and occupation (Bond et al. 1968 ; Heine et al. 2009 ; Singh et al. 2008 ). A possible explanation for this is that external abilities can be more easily identified and measured. Nevertheless, there is also support for the SAH from studies of psychological similarity. These studies demonstrate that people are attracted to others on the perceived basis of shared attitudes (Newcomb 1961 ; Tidwell et al. 2013 ), personality traits (Griffitt 1966 ; Klohnen and Luo 2003 ), and values (Cable and DeRue 2002 ).

In addition to studies measuring the impact of actual similarity, scholars have also looked at the impact of perceived similarity. Many of these studies show that perceived similarities are better predictors of attraction than real similarities (Condon and Crano 1988 ; Montoya et al. 2008 ), but the impact of actual similarity–attraction is mainly restricted to interactions with associates or impressions of “bogus strangers” in laboratory settings (Sunnafrank 1992 ). Montoya et al. ( 2008 ) found a significant impact of actual similarity on attraction, while the strength of the attraction is highly connected to the interaction of the participants and targets (e.g., romantic partner, confederate, bogus stranger); these findings are typically interpreted to mean that demographic similarity information is a single source of information and that over time other information becomes more important than similarity-derived information.

The positive association of similarity to attraction can be explained by social cognition theory (Bandura 1991 ). According to this theory, people make sense of the world around them by gathering information in suitable cognitive classes or conceptual memory bins. This theory highlights how the role played by the categories of schemas, prototypes, and stereotypes biases decision-making reducing the accuracy and objectiveness of judgments (Fiske and Taylor 2008 ). For example, when people perceive someone who has grown up in the same neighborhood as themselves (same sport and school), it results in approval of them as they match a well-formed and well-understood stereotype in the person’s mind. The same person might be much less comfortable with someone from a different racial group or who comes from a completely different area since the appropriate cognitive ‘bin’ is still very immature and might even be distorted due to a few random encounters with similar stimuli. According to social cognition theory, people judge others not based on individual qualities, but rather on the stereotype held regarding that individual’s group membership (Kulik and Bainbridge 2006 ). Self-categorization theory builds on social cognition theory. It says that people assign people to ingroup and outgroup membership of prototypes. People are not thought of as unique individuals but rather as expressions of the relevant prototype, which is a process of depersonalization. Self-categorization theory suggests people classify their membership internally for themselves and others based on socially defined qualities (Turner and Chelladurai 2005 ). Individuals identify themselves and others into in-group and out-group (Hogg and Terry 2000 ) according to their preconceived notions of fit based on the following amongst other things: gender, age, race, organizational membership, and status (Turner and Chelladurai 2005 ).

3 Attraction

One of the vaguer elements in SAH is the definition of the word “attraction’. In the 1500s, the word ‘attraction’ was a medical phrase referring to the body’s tendency to absorb fluids or nourishment (Oxford English Dictionary 2013). Over time, the meaning of this word changed to the ability for an object to draw an object to itself, and then to the capability of a person to draw another person to him or her (Montoya and Horton 2014 ). Although there are a few studies that explore different definitions of the word, there is considerable variation in how it is conceptualized in SAH studies. Some studies emphasize the behavioral dimension (i.e., “drawing one to another,” Schachter 1959 ), other scholars highlight the emotion and affection (i.e., feeling positive towards another; e.g., Zajonc 1968 ), while others stress cognitive aspects (i.e., inferring positive attributes; e.g., Singh et al. 2007 ). Despite the variety, these all have positive connotations (Berscheid 1985 ; Huston and Levinger 1978 ). More recently, the literature has focused on the definition of attraction as an attitude, but limited the definition to an individual’s direct and positive emotional and/or behavioral response to a specific person (Montoya and Horton 2014 ). In this approach, the definition of attraction concentrates on the quality of someone’s emotional reaction to another person and a behavioral element that shows an individual’s tendency to act in a specific way to another (i.e., choosing to move closer to them). According to Montoya and Horton ( 2014 : 60), “attraction is a person’s immediate and positive affective and/or behavioral response to a specific individual, a response that is influenced by the person’s cognitive assessments”. According to this point of view, the cognitive component is not considered part of attraction, but rather a process of forecasting an attraction reaction (Montoya et al. 2018 ).

Hence, the attraction literature has evolved different terms to describe the various attraction elements: affective attraction, behavioral attraction, and interpersonal attraction (or liking), Footnote 1 among other terms (Montoya and Horton 2020 ). Montoya et al. ( 2018 ) applied behavioral attraction to refer entirely to a self-reported preference for a certain behavioral reaction by using liking and interpersonal attraction to refer to an undifferentiated positive evaluation that comprises both affective and behavioral attraction. Reinforcing variation in definition of the word ‘attraction’, Montoya and Horton ( 2020 ) define attraction as an emotion that ranges from the professional, to the romantic, to the familial, meaning that attraction can be operationalized as an emotion in a wide range of settings (Montoya and Horton 2020 ).

4 Methodology

The goal of this study (Kuckertz and Block 2021 ) is to provide an overview of research findings on SAH as they relate to the workplace and, in particular, how they relate to interactions between people at work. To do this, we conducted a systematic review of SAH adopting the PRISMA (i.e., Preferred Reporting Items for Systematic reviews and Meta-Analyses) approach (Caulley et al. 2020 ; O’Dea et al. 2021 ). We conducted a systematic review that looked for both empirical and theoretical papers published in refereed journals with JCR impact factors that investigate SAH. We only included papers written in English. As there had been no previous systematic reviews of SAH, no date limits were set.

4.1 Search methodology

4.1.1 inclusion criteria.

The following search terms were used to identify studies on SAH: ‘similarity-attraction’, ‘similarity attraction’, ‘similarity leads to attraction’, ‘similarity predicts attraction’, ‘similarity-interpersonal attraction’, ‘similarity to attraction’, ‘similarity/attraction’, ‘dissimilarity-repulsion’, ‘dissimilarity leads to repulsion’, ‘dissimilarity/repulsion’, ‘dissimilarity repulsion’, ‘Rosenbaum’s repulsion hypothesis’. Given the specific nature of these search terms, logical operators were used to find at least one of these terms in the titles, subjects, or abstracts of papers. For thoroughness, we included the reverse hypothesis, dissimilarity leads to repulsion (Rosenbaum 1986 ), in our search.

4.1.2 Exclusion criteria

As we employed relatively complex search strings such as ‘similarity-attraction’, exclusion criteria were not needed at the subject level.

4.1.3 Databases searched

The following databases were searched for articles on SAH: PsycInfo and PsycArticles, Web of Science, and Business Search Complete.

4.2 Search results

Table 1 presents the results of the initial trawl of the databases and shows how these 880 articles were filtered. Since the search yielded articles published before the onset of journal rankings and some journals have subsequently merged or ceased publishing, there were papers in the database that would have been excluded because of the evolution of the journal rather than due to their own inherent qualities. To avoid this, we decided to include any paper in a journal without a JCR impact factor that had been published before 2000 and had received 100 or more citations on Google Scholar. This resulted in five additional papers that otherwise would have been excluded from the dataset.

The final stage of filtering was to eliminate papers not located in organizational, business, or management settings in which similarity comparisons were based on staff-staff, staff-team members, staff-leaders, staff-managers, or staff-supervisors. This led to the removal of a further 275 studies and 8 more which were not empirical. 45 studies emerged containing four papers that featured two separate studies. The final database for this systematic review is therefore 45 papers containing 49 separate studies. Figure  1 shows the chronological distribution of the 45 workplace SAH articles in the dataset.

Chronology of workplace SAH papers

Descriptive details and brief summaries of the 49 employee SAH studies surfaced in this systematic review are presented in Tables 3 , 4 , 5 , 6 , 7 and 8 . They are placed in one of three categories. A total of 22 articles explore demographic similarity (Tables 2 and 3 ), 20 studies in 17 papers study psychological similarity (Tables 4 and 5 ), and seven studies in six papers investigate both psychological and demographic similarity (Tables 6 and 7 ). Demographic similarity involves the comparison of surface-level characteristics, such as gender, age, and race. They are permanent, usually observable, and easily measured (Harrison et al. 1998 ; Jackson et al. 1991 ). Psychological similarity involves deep-level attributes such as values, personality, attitudes, and beliefs (e.g., Engle and Lord 1997 ) and tend to be measured through direct assessment of self-reported perceptions.

5.1 Support for SAH

Just over half of the studies in the dataset yield empirical findings broadly in line with SAH predictions. These breakdown as follows: 11 demographic, 11 psychological, and 4 combined studies. In the demographic studies, similarity in gender, race, age, educational level, political affiliation have all been shown repeatedly to (1) positively associate with trust, job satisfaction, affective commitment, and in-role and extra-role performance, and selection decisions, and (2) negatively associate with staff turnover and related exit outcomes. In the psychological similarity studies, a matching pattern of results can be observed when similarity involves personality, emotional intelligence, and leadership and cognitive style. Such studies demonstrate that the SAH applies as much in organizational settings as it does in other settings. Rather, it is the studies that produce contradictory and asymmetric results that provide a more nuanced understanding of how SAH applies in these settings, particularly in studies of demographic similarity.

For example, Geddes and Konrad ( 2003 ) demonstrated a complex set of results in terms of race and performance feedback. They showed that both white and black employees responded more favorably to performance feedback from white managers demonstrating that SAH interacts with effects from other theories; in this case, status characteristics theory (Ridgeway 1991 ; Ridgeway and Balkwell 1997 ; Webster and Hysom 1998 ). Goldberg ( 2005 ) demonstrated a contrarian finding. Both male and female interviewers favored applicants on the opposite sex, which is better explained by social identity theory (Gaertner and Dovidio 2000 ) than SAH. Chatman and O’Reilly ( 2004 ) demonstrated that women reported a greater likelihood of leaving homogenous groups (i.e., groups comprising members of the same gender) than men, suggesting that other factors are in-play such as status conflict (Carli and Eagly 1999 ; Pugh and Wahrman 1983 ). It seems that SAH may be an underlying and natural driver of human behavior, but it is not such a dominant force in organizational settings that it cannot be moderated or eliminated by alternative forces. Although studies have shown that competing forces can influence the emergence of SAH effects, research is needed to understand the causes, circumstances, and conditions that give rise to this submergence. When and why does the SAH not appear in organizational settings?

5.2 What is attraction?

In these studies, there is considerable variation in the ways that attraction has been defined and conceptualized. Examples of the three different definitions of attraction – affective attraction, behavioral attraction, and interpersonal attraction – could be found in the dataset. Very few of the ways in which attraction has been measured might be regarded as direct measurement of attraction. They may be influenced by attractiveness, but most constructs used as interpretations of attraction in these studies are implicit and (at least) one step away from direct and isolated measures of attractiveness. For example, constructs like affective and normative commitment, perceived trustworthiness of managers, reaction to performance feedback, and organizational citizenship behaviors (OCBs) may all be associated with feeling closer (affective attraction), moving closer (behavior attraction), or liking, but many other factors are simultaneously in play and at the very least require some explanation as to the reasons why and how they relate to attraction. Even the more direct conceptualizations like organizational attractiveness or selection outcomes are confounded by other factors such as the scant and managed information typically available during recruitment and selection processes (Billsberry 2007a ; Herriot 1989 ) and other factors influencing attractiveness choices, such as the need to find a job (Billsberry 2007b ). Overall, one of the biggest weaknesses in tests of SAH in organizational settings is the way that attractiveness has been conceived. An analysis of attraction conceptualizations can be found in Table 8 .

Another noteworthy feature of the constructs used to capture attraction is the infrequency of repetition. Other than selection outcomes (of various sorts), perceptions of leadership, and OCBs (which have quite a tenuous association with attraction as there are many and varied reasons why people might engage in extra-role activities), most of the other constructs feature in just one study, occasionally two. This creates a sense that the field is still in an exploratory mode scoping out relevant relationships. Replication studies would provide confidence in these original findings and bring robustness. Further, there is a danger that the inclusiveness of constructs to represent attraction risks reifying the term. A challenge with such reification is that “scholars unknowingly integrate findings from studies with inconsistent construct definitions, which can create serious threats to validity” (Lane et al. 2006 : 835). To avoid this, construct clarity is essential and an important precondition of theory testing (Fisher et al. 2021 ).

5.3 Measurement and design issues

Most work on SAH has been conducted outside of organizations. Our initial searches of these databases generated 880 journal articles of which only 45 were empirical studies in which the participants were workers and data was collected from them or about them. At the fringes of eligibility, we included analyses of the gender composition of top management teams (TMTs) and members of unions. We also included policy capturing studies that involve employees undertaking some type of experiment to find out what they would do in circumstances relevant to their jobs. But we excluded student samples even when they were performing a work-related task as students are too far detached from the reality of work. Our sample was also limited to studies that set out to examine the SAH rather than studies that adopted a SAH to test other relationships, such as value congruence studies (e.g., Cable and DeRue 2002 ). As such, the studies in the present paper typically employed a design in which a similarity independent variable (IV) predicted (or, more commonly, as a result of the dominance of cross-sectional designs, was associated with) an attractiveness dependent variable (DV). This IV → DV relationship was predicted and used to design the empirical study, which was tested with a variation of it (i.e., different types of similarity or attraction variable), typically with moderators or mediators. As such, similarity is viewed as the fundamental driver of effects and there is an absence of studies exploring why similarity is important to people in workplaces.

This dataset is dominated by cross-sectional (13 demographic (59%), 11 psychological (55%), and 4 combined (57%)) studies, which is counter-intuitive given the inherently predictive nature of the SAH (i.e., similarity leads to attraction). Testing the IV and DV at the same time makes the predictive element of the hypothesis unproven. Cross-sectional designs can, at best, show an association of the two constructs and only imply a predictive relationship (Kraemer et al. 2000 ). To get around the confounds in cross-sectional designs, researchers have adopted (1) experimental designs (2 demographic (9%) and 6 psychological (30%)) that capture policy intentions of organizational members, (2) archival studies (6 demographic (27%), 1 psychological (5%), and 1 combined (14%)), which can capture the historical effects on staff turnover and recruitment of similarity, and (3) longitudinal designs (1 demographic similarity, 2 psychological similarity, and 1 combined study). The low number of longitudinal studies in this field clearly presents an opportunity for future research. Such studies could validate the findings of cross-sectional studies, test the predictive nature of the similarity → attraction relationship, and explore the influence of other factors upon it. In addition, longitudinal studies adopting repeated measure methodologies could test for bidirectional effects and look at the strength and duration of the effect; this latter point is important given that Rosenfeld and Jackson ( 1965 ) reported that personality similarity only influenced friendship in the 1st year of acquaintance suggesting that SAH influences the initiation of attraction, but not its long-term survival.

Almost all of the demographic similarity studies in this review operationalize their similarity variables in very stark categorical terms. Gender is male or female; race is skin color and/or ethnographic background. Similarity or difference is calculated as being on one of the predetermined categories. These singular categorizations present a simple view of gender, race, and other demographic similarities. An alternative approach is intersectionality (Crenshaw 1990 ), which argues that different aspects of a person’s identity intersect to influence behavior towards them. For example, to treat Black women like White women ignores many socio-cultural factors disadvantaging them (Wilkins 2012 ). Intersectional analysis could take demographic similarity into a cultural and political space where the purpose of studying demographic SAH is to highlight privilege, disadvantage, and discrimination (Tatli and Özbilgin 2012 ).

5.4 Recruitment and selection

Perhaps the greatest influence of SAH in organizational settings is in explaining recruitment and selection decisions. Various scholars (e.g., Riordan 2000 ; Schneider 1987 ) have argued that applicants are more attracted to, and more expected to choose to work for, an organization whose workforce has features similar to their own. For example, people may be more attracted to a company that recruits a group of employees who are racially like them, predicting that these workers share their values and attitudes (Avery et al. 2004 ). These predictions have been demonstrated to be accurate in several studies in this review. Roth et al. ( 2020 ) showed that candidates’ perceived similarity of their political affiliation influences employment decision-makers, which eventually resulted in liking and organizational citizenship behavior performance (i.e., an employee’s voluntary commitment to the organization beyond his/her contractually obligated tasks). Kacmarek et al. ( 2012 ) found that greater female presence on nominating committees subsequently led to increased female representation on company boards. Chen and Lin ( 2014 ) showed that recruiters’ perceptions of applicants’ similarity to them influenced selection decisions. However, some asymmetric findings have emerged. For example, while Goldberg ( 2005 ) demonstrated race similarity effects, she also noted that male recruiters have a preference for female applicants. But, by and large, these studies align with findings in the general recruitment and selection literature reporting that organizational recruiters favor applicants resembling themselves and this reinforces opportunity in organizations to groups of people who already enjoy employment positions there (Bye et al. 2014 ; Kennedy and Power 2010 ; Noon 2010 ; Persell and Cookson 1985 ; Rivera 2012 ). As a consequence, many countries have passed legislation to protect those disadvantaged by these processes, although such legislation mainly confines itself to surface-level demographic factors such as gender, race, and disability. Hence, the application of SAH to recruitment situations is challenging as it separates demographic and psychological similarity and is located behind a legal mask that probably suppresses its appearance.

5.5 Post-hire

During employment, SAH presents a paradox for organizations. On the one hand, employees like working with people like themselves and are more productive (Bakar and McCann 2014 ; Chatman and O’Reilly 2004 ). But, on the other hand, it creates employee homogeneity, which Schneider ( 1987 ) argues causes organizations to occupy a self-defeating and increasingly narrow ecological niche, and crystalizes disadvantage and discrimination (Dali 2018 ). Empirical studies included in this review demonstrate both sides of this paradox. Interestingly though, looking across the demographic and psychological similarity studies separately, we gained a sense that demographic similarity worked in the initial phases of relationships to bring people together (e.g., Rosenfeld and Jackson 1965 ) and during short engagements (e.g., during recruitment and selection and loan decisions) when demographic similarity aids decision-making when there is impoverished information. Psychological similarity works over more prolonged periods and therefore comes more to the fore in during long-term employment (e.g., Marchiondo et al. 2018 ; Sears and Holmvall, 2010 ).

One noticeable absence in the studies captured in this review are those that explore value congruence (e.g., Adkins et al. 1994 ; Billsberry 2007b ; Chatman 1989 , 1991 ; Meglino et al. 1992 ; Yu and Verma 2019 ). These studies compare the similarity of aspects of people at work, most typically work values, and explore the consequences. Work value congruence has been shown to lead to positive organizational outcomes such as job satisfaction, organizational commitment, decrease employee conflict and negatively related to intentions to leave and organizational exit (Hoffman and Woehr 2006 ; Jehn 1994 ; Jehn et al. 1997 , 1999 ; Kristof-Brown and Guay 2011 ; Subramanian et al. 2022 ; Verquer et al. 2003 ), thereby supporting SAH. Conversely, value incongruence is associated with distancing outcomes such as feelings of not belonging or being unfulfilled, and organizational exit (Edwards and Cable 2009 ; Edwards and Shipp 2007 ; Follmer et al. 2018 ; Kristof-Brown et al. 2005 ; Vogel et al. 2016 ) thereby supporting DRH leading Abbasi et al. ( 2021 ) to suggest that value congruence and value incongruence, and therefore SAH and DRH, are two different forces. The non-appearance of these studies in the current review appears to stem from the way these value congruence studies are theoretically justified. Rather than being direct tests of SAH, they are one step removed and based on ideas of person-environment (PE) fit and person-organization (PO) fit. These theories are themselves grounded in SAH (e.g., Chatman 1989 ; Schneider 1987 ), but the field is sufficiently well developed as a branch of PE and PO fit that it is not necessary to refer back to the conceptual roots of the SAH. This is likely to be the case for many others forms of congruence and incongruence such as political ideology incongruence (e.g., Bermiss and MacDonald 2018 ) or personality congruence (Schneider et al. 1998 ).

5.6 Organizational exit

Perhaps the biggest surprise in this dataset is the almost complete absence of any SAH studies exploring the organizational exit phase of work as voluntary decisions to leave organizations is one of the clearest examples of employees’ behavioral distancing (i.e., moving apart). Furthermore, there is theory arguing (e.g., Schneider 1987 ) that when employees feel dissimilar to others, they leave organizations. As mentioned above, there are many studies in the value congruence, PE fit, and PO fit literatures that explore the effect of dissimilarity on organizational exit, but these are not positioned as direct tests of SAH and so did not appear in this review.

6 Discussion

This systematic review has shown that, by and large, SAH holds true in organizational settings; similarity leads to attraction. There are some contrarian and asymmetrical findings, but these typically occur when other theories interact with SAH. An example comes from Gaertner and Dovidio ( 2000 ) who showed that both male and female interviewers favored applicants of the opposite sex, which is better explained by social identity theory. Further, legal sanctions can influence the appearance of SAH during recruitment, selection, and other episodes. Consequently, SAH can be viewed as a strong underlying force driving employees’ natural behavior, but it is not so strong that it cannot be overcome by other forces.

In organizational settings, paradox surrounds the application of SAH. People have a natural tendency to want to be with people like themselves and, without other influence, will choose to recruit people like themselves. Moreover, they prefer working with people like themselves and are more productive doing so. But such behavior can be exclusionary, discriminatory, and inequitable. Most countries have laws protecting many different categories of people from disadvantageous behavior for this reason. Further, many, perhaps most, organizations have espoused values and adopt policies of equal opportunity and these policies police formal selection processes, performance appraisals, and promotion practices, and informal behavior between employees. Neo-normative organizations go further and espouse values celebrating diversity and inclusivity (Husted 2021 ). In such organizations, employees are encouraged to “just be yourself” (Fleming and Sturdy 2011 : 178), although this does not extend to their natural tendency to want to associate with people like themselves and strict rules exist to ensure compliance (Fleming and Sturdy 2009 ). So, there are many rules and regulations in organizations protecting those who might be disadvantaged from people’s natural tendency to be attracted to people similar to themselves.

Organization-level analysis is missing from the studies included in this systematic review. Instead, all the studies are designed as individual-level studies where data for both IVs and DVs are gathered from or about individuals and the impact of similarity or dissimilarity for them. This aligns with traditions in industrial/organizational psychology (Schneider and Pulakos 2022 ), but although it sheds light on individual differences, it fails to explore the ramifications of similarity and dissimilarity for organizations. This is not an insignificant omission as theoretical work by Schneider ( 1987 ) argues that the similarity-attraction hypothesis is a powerful force creating and reinforcing the cultures of organizations that explains why organizations are different to each other even when in the same industry and location. Further, exploring the impact of individual-level processes at the level of the organization can test assumptions about the importance of individual-level effects for the organization. As Schneider and Pulakos ( 2022 : 386) state, “[t]he problem lies in our tendency to assume that the characteristics that produce high-performing individuals and teams also yield high-performing organizations, without testing this as often as we could or should.” In short, the assumption that effects at the individual-level lead to effects at the organization-level is an ecological fallacy and there is a need to test the organizational impact, if any, of individual-level findings.

Similarly, this systematic review did not include any empirical studies of the dissimilarity-repulsion hypothesis (DRH; Rosenbaum 1986 ). Relevant search terms were included, but no empirical studies of DRH in workplaces were found. One possible explanation accounting for this gap is that researchers may have presumed that since this theory is the polar opposite of similarity-attraction theory, low similarly-attraction means high dissimilarity repulsion, so the testing of both hypotheses may have been viewed as unnecessary. Alternatively, the low number of studies of organizational exit, particularly voluntary turnover, the most natural and powerful repulsion outcome in organizational settings, in this dataset constrains the appearance of the DRH. Organizational exit is a large literature and dissimilarity and misfit are known to be drivers of these actions (e.g., Doblhofer et al. 2019 ; Jackson et al. 1991 ; Kristof-Brown et al. 2005 ), so the suggestion is, like with value congruence, that these studies are grounded on theories other than DRH. Another interesting problem with DRH is the use of the term repulsion, which hints at “abhorrence, loathing, disgust and hatred” (Abbasi et al. 2021 : 9). In times when there is a strong skew towards positive psychology (Kanfer 2005 ), examining the darker side of organizational life is much less common. Nevertheless, these are the moments that have the most impact on people and deserve much greater scholarly attention.

In addition to the above, this systematic review has surfaced several key avenues for future research related to methodological advancement. The first notable weakness in the empirical papers reviewed on this topic is the absence of studies adopting a longitudinal design. This is particularly noteworthy because the similarity leads to attraction hypothesis is inherently predictive in nature. Cross-sectional studies can give a sense of associations between similarity and attraction constructs, but do not convince when applied to predictive hypotheses. Some authors (e.g., Bruns et al. 2008 ; Eagleson et al. 2000 ; Young et al. 1997 ) have circumvented this issue by adopting policy-gathering designs in which respondents give an opinion about what they would do in a particular situation, but the findings of such studies would carry more weight if they were followed up with empirical studies of what people actually do or did in such circumstances. In this literature, there is a tendency for each study to stand separate from the other studies almost as if each study was exploring the hypothesis in a particular aspect of workplaces for the first time. Such exploratory work is commendable, but there is a need for studies to replicate and integrate findings.

The second noticeable methodological weakness in these studies is the manner in which attraction outcomes have been defined. Decisions to join or leave organizations are the most obvious examples of behavioral attraction and repulsion (i.e., ‘moving to’ and ‘moving away’). However, even attraction and repulsion outcomes like these are rarely the exclusive consequence of similarity or dissimilarity, but they have the advantage of being clear movements into or out of organizations. Other behavioral outcomes used in these studies as measures of attraction include absenteeism, TMT homogeneity, diversity levels, advice-seeking, and small business loan decisions, which appear to show decreasing alignment to the notions of attraction or repulsion. The psychological interpretations of attraction are perhaps even further removed from direct definitions of the word. Many constructs have been used as the dependent variables ranging from job satisfaction, perceived PO fit, perceptions of the quality of LMX relationships, commitment to a trade union, reactions to incivility, and employee well-being. These are all important outcomes, but they are not necessarily capturing a sense of feeling closer to others. Only five studies in this sample collected attraction data based on interpersonal liking, relations, or friendship, which might be thought to be the most direct capture of attraction. As a result, the workplace SAH literature gives the impression of actually being a literature that has rigorously explored the impact of many different types of demographic and psychological similarity in organizations, but not necessarily in terms of how it predicts attraction and repulsion outcomes. Hence, there is a strong need for SAH studies that capture more direct measures of attraction and this could prove to be a fruitful avenue for future research.

Another methodological consideration centers on causation. All the studies reviewed in this review employed an IV predicts DV design where the IVs were a form of similarity or dissimilarity and the DVs were a form of attraction or repulsion. As the SAH was being tested, the natural assumption in these studies is that similarity itself is the driver of outcomes. This, of course, triggers questions about why similarity (or dissimilarity) might be driving outcomes. What is it about similarity that drives attraction outcomes? An unkind hypothesis might be that people are inherently racist, sexist, ageist, linguicist, and so forth, in which case management control strategies appear a natural response. A kinder hypothesis is that people feel less threat from people similar to themselves and have a greater sense of belonging amongst people similar to themselves, in which case strategies focused on improving integration appear appropriate. A more neutral hypothesis might be the similarity makes it easier to predict others’ behavior and brings expectations of reciprocity (Newcomb 1956 ), which would raise distributive justice concerns. At present, the SAH in the workplace literature talks little about why similarity drives attraction outcomes and instead tends to focus on whether it does lead to attraction. But as different organizational responses might be expected to follow different causations, it is important to explore the reasons why similarity (and dissimilarity) are having attraction (and repulsion) effects in workplaces.

6.1 Limitations

This systematic review was strictly limited to empirical studies that set out to test SAH (and DRH) in organizational settings. To be included in this systematic review, this intention needed to be stated in the title, abstract, or keywords through the mention of various words and phrases suggesting SAH. This approach means that there are likely to be many studies not included in this systematic review that might measure some form of human similarity and use it to predict some form of attraction. The most obvious examples are the various organizational fit literatures, the value congruence and incongruence literatures, the recruitment, selection, induction, and socialization literatures, and the organizational exit literature. In all these cases, there are theoretical foundations based on SAH or DRH, but these literatures take the SAH and DRH roots ‘as a given’ and have placed their own theoretical frameworks on them (Evertz and Süß 2017 ). For example, an empirical PO fit study, which has similarity assessments at its core, might refer to PE interactions as its theoretical base and not deconstruct this further to the underlying SAH. It seems a common theme in these fields; there is no longer feel the need to justify the study in terms of testing SAH or DRH and therefore these underpinning hypotheses are not mentioned in titles, abstracts, or keywords. Although considerable scholarly advantage would accrue from including these literatures in a systematic review of SAH and DRH, it would be a truly Herculean task as it would require reading literally thousands of papers without any search terms to guide the hunt for similarity IVs and attraction DVs.

7 Conclusion

This paper systematically reviewed empirical studies of the ‘similarity leads to attraction’ (SAH) and ‘dissimilarity leads to repulsion’ (DRH) hypotheses in organizational settings. A total of 49 studies in 45 separate papers were surfaced, which split roughly 50/50 into studies of demographic similarity and psychological similarity. The results of these studies confirm that SAH remains valid in organizational settings and that it is a fundamental force driving employees’ behavior. However, although similarity and dissimilarity drive attraction and repulsion outcomes, the force is not so strong that it cannot be overridden or moderated by other forces, which includes forces from psychological, organizational, and legal domains. This systematic review highlighted a number of methodological issues in tests of SAH relating to the low number of longitudinal studies, which is important given the predictive nature of the hypotheses, and the varying conceptualizations of attraction measurement. This study also demonstrated that paradox is at the heart of SAH in organizational settings.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Code availability

The search terms used to gather data are detailed in the methodology section.

It should be noted that although Montoya and Horton ( 2020 ) categorize ‘liking’ as an element of interpersonal attraction, others have categorized it as an aspect of affective attraction. Differences relate to how ‘liking’ is conceptualized and measured in studies.

Avery DR, Hernandez M, Hebl MR (2004) Who’s watching the race? Racial salience in recruitment advertising. J Appl Soc Psychol 34:146–161. https://doi.org/10.1111/j.1559-1816.2004.tb02541.x

Article   Google Scholar  

*Bacharach SB, Bamberger PA (2004) Diversity and the union: the effect of demographic dissimilarity on members’ union attachment. Group Org Manag 29:385–418. https://doi.org/10.1177/1059601103257414

*Bagues MF, Esteve-Volart B (2010) Can gender parity break the glass ceiling? Evidence from a repeated randomized experiment. Rev Econ Stud 77:1301–1328. https://doi.org/10.1111/j.1467-937X.2009.00601.x

*Bakar HA, McCann RM (2014) Matters of demographic similarity and dissimilarity in supervisor–subordinate relationships and workplace attitudes. Int J Intercultl Relat 41:1–16. https://doi.org/10.1016/j.ijintrel.2014.04.004

Bandura A (1991) Social cognitive theory of self-regulation. Org Behav Human Decis Process 50:248–287. https://doi.org/10.1016/0749-5978(91)90022-l

*Bechtoldt MN (2015) Wanted: self-doubting employees—Managers scoring positively on impostorism favor insecure employees in task delegation. Pers Individ Differ 86:482–486. https://doi.org/10.1016/j.paid.2015.07.002

Bermiss YS, McDonald R (2018) Ideological misfit? Political affiliation and employee departure in the private-equity industry. Acad Manag J 61:2182–2209. https://doi.org/10.5465/amj.2016.0817

Berscheid ES (1985) Interpersonal attraction. In: Lindzey G, Aronson E (eds) Handbook of social psychology, vol 2. Random House, New York, pp 413–483

Google Scholar  

Billsberry J (2007a) Experiencing recruitment and selection. Wiley, Chichester

Billsberry J (2007b) Attracting for values: an empirical study of ASA’s attraction proposition. J Manag Psychol 22:132–149. https://doi.org/10.1108/02683940710726401

Björklund F, Bäckström M, Wolgast S (2012) Company norms affect which traits are preferred in job candidates and may cause employment discrimination. J Psychol 146:579–594. https://doi.org/10.1080/00223980.2012.658459

Bond MH, Byrne D, Diamond MJ (1968) Effect of occupational prestige and attitude similarity on attraction as a function of assumed similarity of attitude. Psychol Rep 23:1167–1172. https://doi.org/10.2466/pr0.1968.23.3f.1167

*Bruns V, Holland DV, Shepherd DA, Woklund J (2008) The role of human capital in loan officers’ decision policies. Entrep Theor Pract 32:485–506. https://doi.org/10.1111/j.1540-6520.2008.00237.x

Bye HH, Horverak JG, Sandal GM, Sam DL, van de Vijver FJR (2014) Cultural fit and ethnic background in the job interview. Int J Cross Cult Manag 14:7–26. https://doi.org/10.1177/1470595813491237

Byrne D (1961) Interpersonal attraction and attitude similarity. J Abnorm Soc Psychol 62:713–715. https://doi.org/10.1037/h0044721

Byrne D (1971) The attraction paradigm. Academic Press, New York

Byrne D (1997) An overview (and underview) of research and theory within the attraction paradigm. J Soc Pers Relat 14:417–431. https://doi.org/10.1177/0265407597143008

Byrne D, Blaylock B (1963) Similarity and assumed similarity among husbands and wives. J Abnorm Soc Psychol 67:636–640. https://doi.org/10.1037/h0045531

Byrne D, Clore GL (1967) Effectance arousal and attraction. J Pers Soc Psychol 6:1–18. https://doi.org/10.1037/h0024829

Byrne D, Nelson D (1965) Attraction as a linear function of proportion of positive reinforcements. J Pers Soc Psychol 1:659–663. https://doi.org/10.1037/h0022073

Byrne D, Clore GL, Worchel P (1966) The effect of economic similarity-dissimilarity on interpersonal attraction. J Pers Soc Psychol 4:220–224. https://doi.org/10.1037/h0023559

Byrne D, Ervin CR, Lamberth J (1970) Continuity between the experimental study of attraction and real-life computing dating. J Pers Soc Psychol 16:157–165. https://doi.org/10.1037/h0029836

Cable DM, DeRue DS (2002) The convergent and discriminant validity of subjective fit perceptions. J Appl Psychol 87:875–884. https://doi.org/10.1037//0021-9010.87.5.875

Carli L, Eagly A (1999) Gender effects on social influence and emergent leadership. In: Powell GN (ed) Handbook of gender and work. Sage, Thousand Oaks, pp 203–222

Chapter   Google Scholar  

Caulley L, Cheng W, Catalá-López F, Whelan J, Khoury M, Ferraro J, Moher D (2020) Citation impact was highly variable for reporting guidelines of health research: a citation analysis. J Clin Epidemiol 127:96–104. https://doi.org/10.1016/j.jclinepi.2020.07.013

Chatman J (1989) Improving interactional organizational research: a model of person-organization fit. Acad Manag Rev 14:333–349. https://doi.org/10.5465/amr.1989.4279063

Chatman JA (1991) Matching people and organizations: selection and socialization in public accounting firms. Adm Sci Q 36:459–484. https://doi.org/10.2307/2393204

*Chatman JA, O’Reilly CA (2004) Asymmetric reactions to work group sex diversity among men and women. Acad Manag J 47:193–208. https://doi.org/10.5465/20159572

*Chen CC, Lin MM (2014) The effect of applicant impression management tactics on hiring recommendations: cognitive and affective processes. Appl Psychol Int Rev 63:698–724. https://doi.org/10.1111/apps.12013

Coates K, Carr SC (2005) Skilled immigrants and selection bias: a theory-based field study from New Zealand. Int J Intercult Relat 29:577–599. https://doi.org/10.1016/j.ijintrel.2005.05.001

Condon JW, Crano WD (1988) Inferred evaluation and the relation between attitude similarity and interpersonal attraction. J Pers Soc Psychol 54:789–797

Crenshaw K (1990) Mapping the margins: intersectionality, identity politics, and violence against women of color. Stan Law Rev 43:1241–1299. https://doi.org/10.2307/1229039

Dali K (2018) Culture fit as anti-diversity Avoiding Human Resources decisions that disadvantage the brightest. Int J Info Divers Incl 2:1–8. https://doi.org/10.33137/ijidi.v2i4.32199

*Den Hartog DN, De Hoogh AHB, Belschak FD (2020) Toot your own horn? Leader narcissism and the effectiveness of employee self-promotion. J Manag 46:261–286. https://doi.org/10.1177/0149206318785240

*Devendorf SA, Highhouse S (2008) Applicant-employee similarity and attraction to an employer. J Occup Org Psychol 81:607–617. https://doi.org/10.1348/096317907X248842

Doblhofer DS, Hauser A, Kuonath A, Haas K, Agthe M, Frey D (2019) Make the best out of the bad: coping with value incongruence through displaying facades of conformity, positive reframing, and self-disclosure. Eur J Work Org Psychol 28:572–593. https://doi.org/10.1080/1359432X.2019.1567579

*Doms M, zu Knyphausen-Aufseß D (2014) Structure and characteristics of top management teams as antecedents of outside executive appointments: a three-country study. Int J Human Resour Manag 25(22):3060–3085. https://doi.org/10.1080/09585192.2014.914052

Duck SW, Craig G (1975) Effects of type of information upon interpersonal attraction. Soc Behav Pers Int J 3:157–164. https://doi.org/10.2224/sbp.1975.3.2.157

*Eagleson G, Waldersee R, Simmons R (2000) Leadership behaviour similarity as a basis of selection into a management team. Br J Soc Psychol 39:301–308. https://doi.org/10.1348/014466600164480

Edwards JR, Cable DM (2009) The value of value congruence. J Appl Psychol 94:654–677. https://doi.org/10.1037/a0014891

Edwards J, Shipp A (2007) The relationship between person-organization fit and outcomes: An integrative theoretical framework. In: Ostroff C, Judge TA (eds) Perspectives on organizational fit. Jossey-Bass, San Francisco, pp 209–258

Engle EM, Lord RG (1997) Implicit theories, self-schemas, and leader-member exchange. Acad Manag J 40:988–1010. https://doi.org/10.2307/256956

Evertz L, Süß S (2017) The importance of individual differences for applicant attraction: a literature review and avenues for future research. Manag Rev Q 67:141–174. https://doi.org/10.1007/s11301-017-0126-2

Festinger L, Schachter S, Back K (1950) Social pressures in informal groups; a study of human factors in housing. Milbank Memorial Fund Q 30:384–387. https://doi.org/10.2307/3348388

Fisher G, Mayer K, Morris S (2021) From the editors—Phenomenon-based theorizing. Acad Manag Rev 46(4):631–639. https://doi.org/10.5465/amr.2021.0320

Fiske ST, Taylor SE (2008) Social cognition: From brains to culture, 3rd edn. McGraw-Hill, New York

Fleming P, Sturdy A (2009) “Just be yourself!: towards neo-normative control in organizations. Emp Relat 31:569–583. https://doi.org/10.1108/01425450910991730

Fleming P, Sturdy A (2011) ‘Being yourself’ in the electronic sweatshop: new forms of normative control. Human Relat 64:177–200. https://doi.org/10.1177/0018726710375481

Follmer EH, Talbot DL, Kristof-Brown AL, Astrove SL, Billsberry J (2018) Resolution, relief, and resignation: a qualitative study of responses to misfit at work. Acad Manag J 61:440–465. https://doi.org/10.5465/amj.2014.0566

Gaertner SL, Dovidio JF (2000) Reducing intergroup bias: The common ingroup identity model. Psychology Press, Philadelphia

Gardiner E (2022) What’s age got to do with it? The effect of board member age diversity: a systematic review. Manag Rev Q. https://doi.org/10.1007/s11301-022-00294-5

*Geddes D, Konrad AM (2003) Demographic differences and reactions to performance feedback. Human Relat 56:1485–1513. https://doi.org/10.1177/00187267035612003

*Georgiou G (2017) Are oral examinations objective? Evidence from the hiring process for judges in Greece. Eur J Law Econ 44:217–239. https://doi.org/10.1007/s10657-016-9545-0

*Goldberg CB (2005) Relational demography and similarity-attraction in interview assessments and subsequent offer decisions - Are we missing something? Group Org Manag 30:597–624. https://doi.org/10.1177/1059601104267661

*Graves LM, Powell GN (1988) An investigation of sex discrimination in recruiters’ evaluations of actual applicants. J Appl Psychol 73:20–29. https://doi.org/10.1037/0021-9010.73.1.20

Graziano WG, Bruce JW (2008) Attraction and the initiation of relationships: A review of the empirical literature. In: Sprecher S, Wenzel A, Harvey J (eds) Handbook of relationship initiation. Psychology Press, Philadelphia, pp 275–301. https://doi.org/10.4324/9780429020513-24

Griffitt WB (1966) Interpersonal attraction as a function of self-concept and personality similarity-dissimilarity. J Pers Soc Psychol 4:581–584. https://doi.org/10.1037/h0023893

Grigoryan L (2020) Perceived similarity in multiple categorization. Appl Psychol Int Rev 69:1122–1144. https://doi.org/10.1111/apps.12202

Hambrick DC (2007) Upper echelons theory: an update. Acad Manag Rev 32:334–343. https://doi.org/10.5465/amr.2007.24345254

Harrison DA, Price KH, Bell MP (1998) Beyond relational demography: time and the effects of surface- and deep-level diversity on work group cohesion. Acad Manag J 41:96–107. https://doi.org/10.5465/256901

Heine SJ, Foster JAB, Spina R (2009) Do birds of a feather universally flock together? Cultural variation in the similarity-attraction effect. Asian J Soc Psychol 12:247–258. https://doi.org/10.1111/j.1467-839x.2009.01289.x

Herriot P (1989) Selection as a social process. In: Smith M, Robertson IT (eds) Advances in selection and assessment. Wiley, Chichester, pp 171–187

Hoffman B, Woehr D (2006) A quantitative review of the relationship between person–organization fit and behavioral outcomes. J Vocat Behav 68:389–399. https://doi.org/10.1016/j.jvb.2005.08.003

Hogg MA, Terry DJ (2000) Social identity and self-categorization processes in organizational contexts. Acad Manag Rev 25:121–140. https://doi.org/10.5465/amr.2000.2791606

Husted E (2021) Alternative organization and neo-normative control: notes on a British town council. Cult Org 27:132–151. https://doi.org/10.1080/14759551.2020.1775595

Huston TL, Levinger G (1978) Interpersonal attraction and relationships. Annu Rev Psychol 29:115–156. https://doi.org/10.1146/annurev.ps.29.020178.000555

Jackson SE, Brett JF, Sessa VI, Cooper DM, Julin JA, Peyronnin K (1991) Some differences make a difference: individual dissimilarity and group heterogeneity as correlates of recruitment, promotions, and turnover. J Appl Psychol 76:675–689. https://doi.org/10.1037/0021-9010.76.5.675

*Jaiswal A, Dyaram L (2019) Towards well-being: role of diversity and nature of work. Emp Relat 41:158–175. https://doi.org/10.1108/ER-11-2017-0279

Jehn KA (1994) Enhancing effectiveness: an investigation of advantages and disadvantages of value-based intragroup conflict. Int J Confl Manag 5:223–238. https://doi.org/10.1108/eb022744

Jehn KA, Chadwick C, Thatcher SM (1997) To agree or not to agree: The effects of value congruence, individual demographic dissimilarity, and conflict on workgroup outcomes. Int J Confl Manag 8:287–305. https://doi.org/10.1108/eb022799

Jehn KA, Northcraft GB, Neale MA (1999) Why differences make a difference: a field study of diversity, conflict, and performance in workgroups. Adm Sci Q 44:741–763. https://doi.org/10.2307/2667054

*Jiang CX, Chua RYJ, Kotabe M, Murray JY (2011) Effects of cultural ethnicity, firm size, and firm age on senior executives’ trust in their overseas business partners: evidence from China. J Int Bus Stud 42:1150–1173. https://doi.org/10.1057/jibs.2011.35

*Kaczmarek S, Kimino S, Pye A (2012) Antecedents of board composition: the role of nomination committees. Corpor Gov Int Rev 20:474–489. https://doi.org/10.1111/j.1467-8683.2012.00913.x

Kanfer R (2005) Self-regulation research in work and I/O psychology. Appl Psychol Int Rev 54:186–191. https://doi.org/10.1111/j.1464-0597.2005.00203.x

Kaptein M, Castaneda D, Fernandez N, Nass C (2014) Extending the similarity-attraction effect: the effects of when-similarity in computer-mediated communication. J Comput Med Commun 19:342–357. https://doi.org/10.1111/jcc4.12049

Kennedy M, Power MJ (2010) The smokescreen of meritocracy: elite education in Ireland and the reproduction of class privilege. J Crit Educ Policy Stud 8:222–248

*Klein KJ, Lim BC, Saltz JL, Mayer DM (2004) How do they get there? An examination of the antecedents of centrality in team networks. Acad Manag J 47:952–963. https://doi.org/10.5465/20159634

Kleinbaum AM, Stuart TE, Tushman ML (2013) Discretion within constraint: homophily and structure in a formal organization. Org Sci 24:1316–1336. https://doi.org/10.1287/orsc.1120.0804

Klohnen EC, Luo S (2003) Interpersonal attraction and personality: what is attractive-self similarity, ideal similarity, complementarity or attachment security? J Pers Soc Psychol 85:709–722. https://doi.org/10.1037/0022-3514.85.4.709

Kraemer HC, Yesavage JA, Taylor JL, Kupfer D (2000) How can we learn about developmental processes from cross-sectional studies, or can we? Am J Psychiatry 157:163–171. https://doi.org/10.1176/appi.ajp.157.2.163

Kristof-Brown AL, Guay RP (2011) Person-environment fit. In: Zedeck S (ed) APA handbook of industrial and organizational psychology, vol 3. American Psychological Association, Washington DC, pp 3–50. https://doi.org/10.1037/12171-001

Kristof-Brown AL, Zimmerman RD, Johnson EC (2005) Consequences of individuals’ fit at work: a meta-analysis of person-job, person-organization, person-group, and person-supervisor fit. Pers Psychol 58:281–342. https://doi.org/10.1111/j.1744-6570.2005.00672.x

Kuckertz A, Block J (2021) Reviewing systematic literature reviews: ten key questions and criteria for reviewers. Manag Rev Q 71:519–524. https://doi.org/10.1007/s11301-021-00228-7

Kulik CT, Bainbridge HT (2006) HR and the line: the distribution of HR activities in Australian organisations. Asia Pac J Human Resour 44:240–256. https://doi.org/10.1177/1038411106066399

Lane PJ, Pathak KBR, S, (2006) The reification of absorptive capacity: a critical review and rejuvenation of the construct. Acad Manag Rev 31:833–863. https://doi.org/10.5465/amr.2006.22527456

*Lau DC, Lam LW, Salamon SD (2008) The impact of relational demographics on perceived managerial trustworthiness: similarity or norms? J Soc Psychol 148:187–209. https://doi.org/10.3200/socp.148.2.187-209

*Liang HY, Shih HA, Chiang YH (2015) Team diversity and team helping behavior: the mediating roles of team cooperation and team cohesion. Eur Manag J 33:48–59. https://doi.org/10.1016/j.emj.2014.07.002

*Marchiondo LA, Biermeier-Hanson B, Krenn DR, Kabat-Farr D (2018) Target meaning-making of workplace incivility based on perceived personality similarity with perpetrators. J Psychol Interdiscip Appl 152:474–496. https://doi.org/10.1080/00223980.2018.1481819

*Marstand AF, Epitropaki O, Martin R (2018) Cross-lagged relations between perceived leader–employee value congruence and leader identification. J Occup Org Psychol 91:411–420. https://doi.org/10.1111/joop.12192

McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Annu Rev Soc 27:415–444. https://doi.org/10.1146/annurev.soc.27.1.415

Meglino BM, Ravlin EC, Adkins CL (1992) The measurement of work value congruence: a field study comparison. J Manag 18:33–43. https://doi.org/10.1177/014920639201800103

*Mitteness CR, DeJordy R, Ahuja MK, Sudek R (2016) Extending the role of similarity attraction in friendship and advice networks in angel groups. Entrep Theor Pract 40:627–655. https://doi.org/10.1111/etap.12135

Montoya RM, Horton RS (2012) The reciprocity of liking effect. In: Paludi M (ed) The psychology of love. Praeger, Santa Barbara, pp 39–57

Montoya RM, Horton RS (2013) A meta-analytic investigation of the processes underlying the similarity-attraction effect. J Soc Pers Rel 30:64–94. https://doi.org/10.1177/0265407512452989

Montoya RM, Horton RS (2014) A two-dimensional model for the study of interpersonal attraction. Pers Soc Psychol Rev 18:59–86. https://doi.org/10.1177/1088868313501887

Montoya RM, Horton RS (2020) Understanding the attraction process. Soc Pers Psychol Compass 14:e12526. https://doi.org/10.1111/spc3.12526

Montoya RM, Horton RS, Kirchner J (2008) Is actual similarity necessary for attraction? A meta-analysis of actual and perceived similarity. J Soc Pers Relat 25:889–922. https://doi.org/10.1177/0265407508096700

Montoya RM, Kershaw C, Prosser JL (2018) A meta-analytic investigation of the relation between interpersonal attraction and enacted behavior. Psychol Bull 144:673–709. https://doi.org/10.1037/bul0000148

Newcomb TM (1956) The prediction of interpersonal attraction. Am Psychol 11:575–586. https://doi.org/10.1037/h0046141

Newcomb TM (1961) The acquaintanceship process. Holt, New York

Book   Google Scholar  

*Nielsen S (2009) Why do top management teams look the way they do? A multilevel exploration of the antecedents of TMT heterogeneity. Strateg Org 7:277–305. https://doi.org/10.1177/1476127009340496

Noon M (2010) The shackled runner: time to rethink positive discrimination? Work Employ Soc 24:728–739. https://doi.org/10.1177/0950017010380648

O’Dea RE, Lagisz M, Jennions MD, Koricheva J, Noble DW, Parker TH, Nakagawa S (2021) Preferred reporting items for systematic reviews and meta-analyses in ecology and evolutionary biology: a PRISMA extension. Biol Rev 96:1695–1722. https://doi.org/10.1111/brv.12721

O’Reilly CA III, Caldwell DF, Chatman JA, Doerr B (2014) The promise and problems of organizational culture: CEO personality, culture, and firm performance. Group Org Manag 39:595–625. https://doi.org/10.1177/1059601114550713

*Orpen C (1984) Attitude similarity, attraction, and decision-making in the employment interview. J Psychol 117:111–120. https://doi.org/10.1080/00223980.1984.9923666

*Parent-Rocheleau X, Bentein K, Simard G (2020) Positive together? The effects of leader-follower (dis) similarity in psychological capital. J Bus Res 110:435–444. https://doi.org/10.1016/j.jbusres.2020.02.016

*Perry EL, Kulik CT, Zhou J (1999) A closer look at the effects of subordinate-supervisor age differences. J Org Behav 20:341–357. https://doi.org/10.1002/(sici)1099-1379(199905)20:3%3c341::aid-job915%3e3.0.co;2-d

Persell CH, Cookson P (1985) Chartering and bartering: elite education and social reproduction. Soc Probl 33:114–129. https://doi.org/10.2307/800556

Pierce CA, Byrne D, Aguinis H (1996) Attraction in organizations: a model of workplace romance. J Org Behav 17:5–32. https://doi.org/10.1002/(SICI)1099-1379(199601)17:1%3c5::AID-JOB734%3e3.0.CO;2-E

Pugh M, Wahrman R (1983) Neutralizing sexism in mixed-sex groups: do women have to be better than men? Am J Soc 88:746–762. https://doi.org/10.1086/227731

Ridgeway CL (1991) The social construction of status value: gender and other nominal characteristics. Soc Forces 70:367–386. https://doi.org/10.1093/sf/70.2.367

Ridgeway CL, Balkwell JW (1997) Group processes and the diffusion of status beliefs. Soc Psychol Q 60:14–31. https://doi.org/10.2307/2787009

Riordan CM (2000) Relational demography within groups: Past developments, contradictions, and new directions. In: Ferris GR (ed) Research in personnel and human resources management, vol 19. JAI Press, New York, pp 131–174

Rivera LA (2012) Hiring as cultural matching: the case of elite professional service firms. Am Soc Rev 77:999–1022. https://doi.org/10.1177/0003122412463213

Rosenbaum ME (1986) The repulsion hypothesis. On the nondevelopment of relationships. J Pers Soc Psychol 51:1156–1166. https://doi.org/10.1037/0022-3514.51.6.1156

*Rosenfeld HM, Jackson J (1965) Temporal mediation of the similarity-attraction hypothesis. J Pers 33:649–656. https://doi.org/10.1111/j.1467-6494.1965.tb01410.x

*Roth PL, Thatcher JB, Bobko P, Matthews KD, Ellingson JE, Goldberg CB (2020) Political affiliation and employment screening decisions: the role of similarity and identification processes. J Appl Psychol 105:472–486. https://doi.org/10.1037/apl0000422

*Rupert J, Jehn KA, van Engen ML, de Reuver RS (2010) Commitment of cultural minorities in organizations: effects of leadership and pressure to conform. J Bus Psychol 25:25–37. https://doi.org/10.1007/s10869-009-9131-3

Sacco JM, Scheu CR, Ryan AM, Schmitt N (2003) An investigation of race and sex similarity effects in interviews: a multi-level approach to relational demography. J Appl Psychol 88:852–865. https://doi.org/10.1037/0021-9010.88.5.852

*Salminen M, Henttonen P, Ravaja N (2016) The role of personality in dyadic interaction: a psychophysiological study. Int J Psychophysiol 109:45–50. https://doi.org/10.1016/j.ijpsycho.2016.09.014

Schachter S (1959) The psychology of affiliation. Stanford University Press, Stanford

*Schieman S, McMullen T (2008) Relational demography in the workplace and health: an analysis of gender and the subordinate-superordinate role-set. J Health Soc Behav 49:286–300. https://doi.org/10.1177/002214650804900304

Schneider B (1987) The people make the place. Pers Psychol 40:437–453. https://doi.org/10.1111/j.1744-6570.1987.tb00609.x

Schneider B, Pulakos ED (2022) Expanding the I-O psychology mindset to organizational success. Ind Organ Psychol 15:385–402. https://doi.org/10.1017/iop.2022.27

Schneider B, Smith DB, Taylor S, Fleenor J (1998) Personality and organizations: a test of the homogeneity of personality hypothesis. J Appl Psychol 83:462–470. https://doi.org/10.1037/0021-9010.83.3.462

*Schreurs B, Druart C, Proost K, De Witte K (2009) Symbolic attributes and organizational attractiveness: the moderating effects of applicant personality. Int J Sel Assess 17:35–46. https://doi.org/10.1111/j.1468-2389.2009.00449.x

*Sears GJ, Holmvall CM (2010) The joint influence of supervisor and subordinate emotional intelligence on leader–member exchange. J Bus Psychol 25:593–605. https://doi.org/10.1007/s10869-009-9152-y

Singh R, Ho LJ, Tan HL, Bell PA (2007) Attitudes, personal evaluations, cognitive evaluation, and interpersonal attraction: on the direct, indirect, and reverse-causal effects. Br J Soc Psychol 46:19–42. https://doi.org/10.1348/014466606x104417

Singh R, Lin PKF, Tan HL, Ho LJ (2008) Evaluations, attitude similarity, and interpersonal attraction: testing the hypothesis of weighting interference across responses. Basic Appl Soc Psychol 30:241–252. https://doi.org/10.1080/01973530802375052

*Stark E, Poppler P (2009) Leadership, performance evaluations, and all the usual suspects. Pers Rev 38:320–338. https://doi.org/10.1108/00483480910943368

Subramanian S, Billsberry J, Barrett M (2022) A bibliometric analysis of person-organization fit research: significant features and contemporary trends. Manag Rev Q. https://doi.org/10.1007/s11301-022-00290-9

Sunnafrank M (1992) On debunking the attitude similarity myth. Commun Monogr 59:164–179. https://doi.org/10.1080/03637759209376259

Tatli A, Özbilgin MF (2012) An emic approach to intersectional study of diversity at work: a Bourdieuan framing. Int J Manag Rev 14:180–200. https://doi.org/10.1111/j.1468-2370.2011.00326.x

Terman LM (1938) Psychological factors in marital happiness. McGraw-Hill, New York

Tidwell ND, Eastwick PW, Finkel EJ (2013) Perceived, not actual, similarity predicts initial attraction in a live romantic context: evidence from the speed-dating paradigm. Pers Relat 20:199–215. https://doi.org/10.1111/j.1475-6811.2012.01405.x

*Tsui AS, Porter LW, Egan TD (2002) When both similarities and dissimilarities matter: extending the concept of relational demography. Human Relat 55:899–929. https://doi.org/10.1177/0018726702055008176

Turner BA, Chelladurai P (2005) Organizational and occupational commitment, intention to leave, and perceived performance of intercollegiate coaches. J Sport Manag 19:193–211. https://doi.org/10.1123/jsm.19.2.193

*Van Hoye G, Turban DB (2015) Applicant–employee fit in personality: testing predictions from similarity-attraction theory and trait activation theory. Int J Sel Assess 23:210–223. https://doi.org/10.1111/ijsa.12109

Verquer ML, Beehr TA, Wagner SH (2003) A meta-analysis of relations between person–organization fit and work attitudes. J Vocat Behav 63:473–489. https://doi.org/10.1016/S0001-8791(02)00036-2

Vogel RM, Rodell JB, Lynch JW (2016) Engaged and productive misfits: how job crafting and leisure activity mitigate the negative effects of value incongruence. Acad Manag J 59:1561–1584. https://doi.org/10.5465/amj.2014.0850

Walster E, Aronson V, Abrahams D, Rottman L (1966) Importance of physical attractiveness in dating behavior. J Pers Soc Psychol 4:508–516. https://doi.org/10.1037/h0021188

Webster M Jr, Hysom SJ (1998) Creating status characteristics. Am Soc Rev. https://doi.org/10.2307/2657554

Wilkins AC (2012) Becoming black women: intimate stories and intersectional identities. Soc Psychol Q 75:173–196. https://doi.org/10.1177/0190272512440106

*Xu J, Yun K, Yan F, Jang P, Kim J, Pang C (2019) A study on the effect of TMT characteristics and vertical dyad similarity on enterprise achievements. Sustain 11:1–16. https://doi.org/10.3390/su11102913

*Young IP, Place AW, Rinehart JS, Jury JC, Baits DF (1997) Teacher recruitment: a test of the similarity-attraction hypothesis for race and sex. Educ Adm Q 33:86–106. https://doi.org/10.1177/0013161X97033001005

Yu KYT, Verma K (2019) Investigating the role of goal orientation in job seekers’ experience of value congruence. Appl Psychol Int Rev 68:83–125. https://doi.org/10.1111/apps.12152

Zajonc RB (1968) Attitudinal effects of mere exposure. J Pers Soc Psychol Monogr 9:1–27. https://doi.org/10.1037/h0025848

*Zatzick CD, Elvira MM, Cohen LE (2003) When is more better? The effects of racial composition on voluntary turnover. Org Sci 14:483–496. https://doi.org/10.1287/orsc.14.5.483.16768

*Zhu DH, Chen G (2015) Narcissism, director selection, and risk-taking spending. Strateg Manag J 36:2075–2098. https://doi.org/10.1002/smj.2322

* Included in systematic review

Abbasi Z, Billsberry J, Todres M (2021) An integrative conceptual two-factor model of workplace value congruence and incongruence. Manag Res Rev. https://doi.org/10.1108/MRR-03-2021-0211

Adkins CL, Russell CJ, Werbel JD (1994) Judgments of fit in the selection process: the role of work value congruence. Pers Psychol 47:605–623. https://doi.org/10.1111/j.1744-6570.1994.tb01740.x

Alliger GM, Janak EA, Streeter D, Byrne D, Turban D (1993) Psychological similarity effects in personnel selection decisions and work relations: A meta-analysis. In: Annual conference of the society for industrial and organizational psychology, San Francisco CA, USA.

*Allinson CW, Armstrong SJ, Hayes J (2001) The effects of cognitive style on leader-member exchange: a study of manager-subordinate dyads. J Occup Org Psychol 74:201–220. https://doi.org/10.1348/096317901167316

*Almeida S, Fernando M, Hannif Z, Dharmage SC (2015) Fitting the mould: the role of employer perceptions in immigrant recruitment decision-making. Int J Human Resour Manag 26:2811–2832. https://doi.org/10.1080/09585192.2014.1003087

*Aronson ZH, Dominick PG, Wang M (2014) Exhibiting leadership and facilitation behaviors in NPD project-based work: does team personal style composition matter? Eng Manag J 26:25–35. https://doi.org/10.1080/10429247.2014.11432017

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Abbasi, Z., Billsberry, J. & Todres, M. Empirical studies of the “similarity leads to attraction” hypothesis in workplace interactions: a systematic review. Manag Rev Q (2023). https://doi.org/10.1007/s11301-022-00313-5

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New hypothesis emerges on Parkinson's disease's origins and spread

T he nose or the gut? For the past two decades, the scientific community has debated the wellspring of the toxic proteins at the source of Parkinson's disease. In 2003, a German pathologist, Heiko Braak, MD, first proposed that the disease begins outside the brain.

More recently, Per Borghammer, MD, with Aarhus University Hospital in Denmark, and his colleagues argue that the disease is the result of processes that start in either the brain's smell center (brain-first) or the body's intestinal tract (body-first).

A new hypothesis paper appearing in the Journal of Parkinson's Disease on World Parkinson's Day unites the brain- and body-first models with some of the likely causes of the disease–environmental toxicants that are either inhaled or ingested.

The authors of the new study, who include Borghammer, argue that inhalation of certain pesticides, common dry cleaning chemicals, and air pollution predispose to a brain-first model of the disease. Other ingested toxicants, such as tainted food and contaminated drinking water, lead to body-first model of the disease.

"In both the brain-first and body-first scenarios the pathology arises in structures in the body closely connected to the outside world," said Ray Dorsey, MD, a professor of Neurology at the University of Rochester Medical Center and co-author of the piece.

"Here we propose that Parkinson's is a systemic disease and that its initial roots likely begin in the nose and in the gut and are tied to environmental factors increasingly recognized as major contributors, if not causes, of the disease. This further reinforces the idea that Parkinson's, the world's fastest growing brain disease, may be fueled by toxicants and is therefore largely preventable."

Different pathways to the brain, different forms of disease

A misfolded protein called alpha-synuclein has been in scientists' sights for the last 25 years as one of the driving forces behind Parkinson's. Over time, the protein accumulates in the brain in clumps, called Lewy bodies, and causes progressive dysfunction and death of many types of nerve cells, including those in the dopamine-producing regions of the brain that control motor function. When first proposed, Braak thought that an unidentified pathogen, such as a virus, may be responsible for the disease.

The new piece argues that toxins encountered in the environment, specifically the dry cleaning and degreasing chemicals trichloroethylene (TCE) and perchloroethylene (PCE), the weed killer paraquat, and air pollution, could be common causes for the formation of toxic alpha-synuclein.

TCE and PCE contaminates thousands of former industrial, commercial, and military sites, most notably the Marine Corps base Camp Lejeune, and paraquat is one of the most widely used herbicides in the US, despite being banned for safety concerns in more than 30 countries, including the European Union and China. Air pollution was at toxic levels in nineteenth century London when James Parkinson, whose 269th birthday is celebrated today, first described the condition.

The nose and the gut are lined with a soft permeable tissue, and both have well established connections to the brain. In the brain-first model, the chemicals are inhaled and may enter the brain via the nerve responsible for smell. From the brain's smell center, alpha-synuclein spreads to other parts of the brain principally on one side, including regions with concentrations of dopamine-producing neurons.

The death of these cells is a hallmark of Parkinson's disease. The disease may cause asymmetric tremor and slowness in movement and, a slower rate of progression after diagnosis, and only much later, significant cognitive impairment or dementia.

When ingested, the chemicals pass through the lining of the gastrointestinal tract. Initial alpha-synuclein pathology may begin in the gut's own nervous system from where it can spread to both sides of the brain and spinal cord.

This body-first pathway is often associated with Lewy body dementia, a disease in the same family as Parkinson's, which is characterized by early constipation and sleep disturbance, followed by more symmetric slowing in movements and earlier dementia, as the disease spreads through both brain hemispheres.

New models to understand and study brain diseases

"These environmental toxicants are widespread and not everyone has Parkinson's disease," said Dorsey. "The timing, dose, and duration of exposure and interactions with genetic and other environmental factors are probably key to determining who ultimately develops Parkinson's. In most instances, these exposures likely occurred years or decades before symptoms develop."

Pointing to a growing body of research linking environmental exposure to Parkinson's disease, the authors believe the new models may enable the scientific community to connect specific exposures to specific forms of the disease. This effort will be aided by increasing public awareness of the adverse health effects of many chemicals in our environment.

The authors conclude that their hypothesis "may explain many of the mysteries of Parkinson's disease and open the door toward the ultimate goal–prevention."

In addition to Parkinson's, these models of environmental exposure may advance understanding of how toxicants contribute to other brain disorders, including autism in children, ALS in adults, and Alzheimer's in seniors.

Dorsey and his colleagues at the University of Rochester have organized a symposium on the Brain and the Environment in Washington, DC, on May 20 that will examine the role toxicants in our food, water, and air are playing in all these brain diseases. Additional authors of the hypothesis paper include Briana De Miranda, Ph.D., with the University of Alabama at Birmingham, and Jacob Horsager, MD, Ph.D., with Aarhus University Hospital in Denmark.

More information: E. Ray Dorsey et al, The Body, the Brain, the Environment, and Parkinson's Disease, Journal of Parkinson's Disease (2024). DOI: 10.3233/JPD-240019

Provided by University of Rochester Medical Center