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What Is a Testable Hypothesis?

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A hypothesis is a tentative answer to a scientific question. A testable hypothesis is a  hypothesis that can be proved or disproved as a result of testing, data collection, or experience. Only testable hypotheses can be used to conceive and perform an experiment using the scientific method .

Requirements for a Testable Hypothesis

In order to be considered testable, two criteria must be met:

• It must be possible to prove that the hypothesis is true.
• It must be possible to prove that the hypothesis is false.
• It must be possible to reproduce the results of the hypothesis.

Examples of a Testable Hypothesis

All the following hypotheses are testable. It's important, however, to note that while it's possible to say that the hypothesis is correct, much more research would be required to answer the question " why is this hypothesis correct?" 

• Students who attend class have higher grades than students who skip class.  This is testable because it is possible to compare the grades of students who do and do not skip class and then analyze the resulting data. Another person could conduct the same research and come up with the same results.
• People exposed to high levels of ultraviolet light have a higher incidence of cancer than the norm.  This is testable because it is possible to find a group of people who have been exposed to high levels of ultraviolet light and compare their cancer rates to the average.
• If you put people in a dark room, then they will be unable to tell when an infrared light turns on.  This hypothesis is testable because it is possible to put a group of people into a dark room, turn on an infrared light, and ask the people in the room whether or not an infrared light has been turned on.

Examples of a Hypothesis Not Written in a Testable Form

• It doesn't matter whether or not you skip class.  This hypothesis can't be tested because it doesn't make any actual claim regarding the outcome of skipping class. "It doesn't matter" doesn't have any specific meaning, so it can't be tested.
• Ultraviolet light could cause cancer.  The word "could" makes a hypothesis extremely difficult to test because it is very vague. There "could," for example, be UFOs watching us at every moment, even though it's impossible to prove that they are there!
• Goldfish make better pets than guinea pigs.  This is not a hypothesis; it's a matter of opinion. There is no agreed-upon definition of what a "better" pet is, so while it is possible to argue the point, there is no way to prove it.

How to Propose a Testable Hypothesis

Now that you know what a testable hypothesis is, here are tips for proposing one.

• Try to write the hypothesis as an if-then statement. If you take an action, then a certain outcome is expected.
• Identify the independent and dependent variable in the hypothesis. The independent variable is what you are controlling or changing. You measure the effect this has on the dependent variable.
• Write the hypothesis in such a way that you can prove or disprove it. For example, a person has skin cancer, you can't prove they got it from being out in the sun. However, you can demonstrate a relationship between exposure to ultraviolet light and increased risk of skin cancer.
• Make sure you are proposing a hypothesis you can test with reproducible results. If your face breaks out, you can't prove the breakout was caused by the french fries you had for dinner last night. However, you can measure whether or not eating french fries is associated with breaking out. It's a matter of gathering enough data to be able to reproduce results and draw a conclusion.
• What Are Examples of a Hypothesis?
• What Is a Hypothesis? (Science)
• What Are the Elements of a Good Hypothesis?
• Scientific Method Flow Chart
• Null Hypothesis Examples
• Scientific Hypothesis Examples
• Understanding Simple vs Controlled Experiments
• Six Steps of the Scientific Method
• Scientific Method Vocabulary Terms
• What Is a Controlled Experiment?
• Scientific Variable
• What Is an Experimental Constant?
• What Is the Difference Between a Control Variable and Control Group?
• DRY MIX Experiment Variables Acronym
• Random Error vs. Systematic Error
• The Role of a Controlled Variable in an Experiment

Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

Identifying Testable Hypotheses: A Guide To Verifiable Scientific Claims

• by Carlos Manuel Alcocer
• September 24, 2024 June 18, 2024

A testable hypothesis is a specific, empirically testable statement that predicts the relationship between two or more variables. It includes an independent variable (manipulated by the researcher), a dependent variable (measured or observed), and a clear prediction about the expected outcome. Hypotheses are essential for guiding research, as they provide a framework for designing experiments, collecting data, and drawing conclusions. They should be specific, falsifiable, and based on prior research or theoretical knowledge.

Table of Contents

The Anatomy of a Hypothesis: Delving into Variables and Testability

Hypothesis: The Guiding Light of Research

A hypothesis is a tentative explanation or prediction that serves as the foundation of scientific inquiry. It’s a roadmap that guides researchers toward their destination: answering research questions. Variables , like characters in a play, are the entities being studied and analyzed within a hypothesis.

The independent variable is the one that the researcher manipulates or changes, like the amount of fertilizer applied to a plant. The dependent variable is the one that responds to the changes in the independent variable, like the height of the plant.

Testability: The Proof in the Pudding

For a hypothesis to be testable , it must meet certain criteria. It should be specific enough to allow for empirical observation or experimentation, like growing plants with different amounts of fertilizer. The hypothesis should also be falsifiable, meaning it can be disproven if the results don’t support it.

Independent vs. Dependent: A Dynamic Duet

The relationship between the independent and dependent variables is crucial. By manipulating the independent variable, researchers can observe how it affects the dependent variable. This allows them to draw conclusions about cause and effect, for instance, seeing how changes in fertilizer amount impact plant growth.

Control Variables: The Unsung Heroes

Often, there are other control variables that need to be accounted for to eliminate their potential影響 on the dependent variable. For instance, in our plant growth experiment, researchers might control for light intensity to ensure that it doesn’t skew the results.

Testable Hypothesis: Unveiling the Essential Components

In the realm of scientific inquiry, formulating a testable hypothesis is pivotal to unraveling the mysteries of the world. A testable hypothesis is not merely a guess or an assumption; it’s a meticulously crafted statement that can be put to the test using empirical observation or experimentation .

At the heart of a testable hypothesis lies the null hypothesis and the alternative hypothesis. The null hypothesis proposes that there is no significant difference or relationship between the variables being investigated. In contrast, the alternative hypothesis asserts that there is a significant difference or relationship. These two hypotheses form the foundation for testing the validity of the initial assumption.

To construct a testable hypothesis, researchers also formulate a prediction . This prediction outlines the expected outcome if the alternative hypothesis is supported by evidence. By making a prediction, researchers can design experiments or observations that will either validate or refute their hypothesis.

The importance of empirical observation or experimentation cannot be overstated in testing hypotheses. Through these methods, researchers gather objective data that can be analyzed to determine whether the evidence supports the alternative hypothesis or the null hypothesis. Empirical observation involves directly witnessing and recording events in a controlled setting, while experimentation involves manipulating variables and studying their effects.

By formulating testable hypotheses and conducting rigorous empirical observations or experiments, researchers can confidently draw conclusions about the world around them. These conclusions can advance our knowledge, inspire new discoveries, and ultimately shape our understanding of the universe we inhabit.

Research Questions: The Guiding Light in Your Scientific Journey

Hypothesis Testing: A Keystone in Research

Before delving into the intriguing world of hypothesis testing, it’s essential to establish a firm foundation. A hypothesis, simply put, is a tentative explanation for a phenomenon. To be scientifically testable, this hypothesis must contain an independent variable , a dependent variable , and a clear prediction.

Testable Hypotheses: A Path to Verification or Rejection

A testable hypothesis is the lifeblood of scientific inquiry. It consists of a null hypothesis (H0), which assumes no significant difference or effect, and an alternative hypothesis (H1), which proposes the opposite. This framework allows us to empirically test our hypotheses, relying on observation or experimentation. The results of these tests will either support the alternative hypothesis or fail to reject the null hypothesis.

Research Questions: The Gateway to Specific Inquiries

Research questions are the driving force behind scientific investigations. They articulate specific inquiries that guide the research process and provide direction for formulating hypotheses. These questions are closely intertwined with hypotheses, variables, and data, forming an interconnected web of scientific exploration.

Hypothesis, Variables, Data: An Inseparable Trinity

The hypothesis identifies the variables , which are the measurable factors under investigation. The independent variable is the one manipulated or changed by the researcher, while the dependent variable is the one that is observed or measured in response.

The interconnectedness of hypothesis, variables, and data is pivotal. The research question shapes the hypothesis, which in turn dictates the selection of appropriate variables. The hypothesis and variables guide the collection of data , which is the raw material for analysis and the ultimate foundation for drawing conclusions.

In summary, research questions serve as the compass for scientific investigations, guiding the development of testable hypotheses and the collection of relevant data. Without clear and well-defined research questions, scientific inquiry would lack direction and purpose.

Variables: The Players in Hypothesis Testing

In the world of scientific research, variables play a crucial role in unraveling the mysteries around us. Think of them as the actors and actresses in the grand play of hypothesis testing, each with their unique role to fulfill.

At the heart of every hypothesis lies the independent variable , the one we manipulate or change to see its effect on something else. Picture a scientist studying the impact of caffeine on sleep patterns. The independent variable here is the amount of caffeine consumed.

On the receiving end of this manipulation is the dependent variable , the one we observe to measure the impact of the independent variable. In our caffeine study, the dependent variable would be the duration and quality of sleep.

But wait, there’s more! To ensure the accuracy of our findings, we need to control for other factors that could influence the dependent variable. Enter the control variables . These are variables we keep constant or minimize their impact to isolate the effect of the independent variable. Age, gender, and sleep environment are common control variables in our caffeine experiment.

Controlling variables is like a magician’s trick. By eliminating other potential influences, we can focus on the true relationship between the independent and dependent variables. This allows us to draw more accurate conclusions about the impact of our manipulation.

Data: The Heart of Hypothesis Testing

Understanding the data you collect is crucial in hypothesis testing. Data provides the empirical evidence to support or refute your claims. There are two primary types of data: quantitative and qualitative.

Quantitative Data: Numbers and statistics tell a story. Quantitative data is measurable, numerical information that can be analyzed statistically. Examples include test scores, blood pressure readings, and time measurements.

Qualitative Data: Not all data can be measured in numbers. Qualitative data provides rich insights into experiences, emotions, and opinions. Interviews, observations, and written accounts are examples of qualitative data.

Primary vs. Secondary Data: The source of your data also matters. Primary data is collected firsthand by the researcher, while secondary data has been previously collected by others. Primary data is more relevant to your research question, but it can be time-consuming to collect. Secondary data is readily available, but it may not be as specific to your research needs.

Carlos Manuel Alcocer is a seasoned science writer with a passion for unraveling the mysteries of the universe. With a keen eye for detail and a knack for making complex concepts accessible, Carlos has established himself as a trusted voice in the scientific community. His expertise spans various disciplines, from physics to biology, and his insightful articles captivate readers with their depth and clarity. Whether delving into the cosmos or exploring the intricacies of the microscopic world, Carlos’s work inspires curiosity and fosters a deeper understanding of the natural world.

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What Is A Research Hypothesis?

A Plain-Language Explainer + Practical Examples

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.

Need a helping hand?

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:

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.

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18 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?

Angelo Loye Very fantastic information. From here I am going straightaway to present the research hypothesis One question, do we apply hypothesis in qualitative research? What nul hypothesi Otherwise I appreciate your research methodo

this is very important note help me much more

Hi” best wishes to you and your very nice blog” 

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Probability and Hypothesis Testing

Hypothesis testing is a process by which data are analyzed and conclusions are drawn about whether the results support or refute the hypothesis. This process allows statisticians to determine the likelihood that their results are not due to chance and, instead, likely represent truths about populations that are in keeping with their hypotheses. Note that the tenet of probability (introduced in Chapter 1) is a component of this process. When a hypothesis is tested, steps are followed and calculations are performed to assess the probability (likelihood) that a hypothesis is true based on sample data. Thus, the terms supported and refuted are used instead of the words proven and disproven, respectively.

Hypotheses come in a variety of forms, each of which requires different statistical methods of analysis. Some hypotheses, like the one about oatmeal and cholesterol from earlier in this chapter, state that a treatment condition (or intervention) will cause a difference in a measurable outcome variable. To review, that hypothesis written in sentence and symbol formats is:

Cholesterol levels will be lower after (post) eating oatmeal daily for six weeks compared to before (pre).

\(H_a: \mu_{\text {post }}<\mu_{\text {pre }}\)

In this example daily oatmeal consumption is the treatment condition and cholesterol level is the outcome being measured. We can see from the symbol format that data will need to be collected from a sample both before the treatment in order to compute a pretest mean and again from the same sample after the treatment in order to compute a posttest mean. The means can then be compared to see if, as is expected based on the hypothesis, the mean cholesterol level for the sample is lower at posttest than it was at pretest.

It is tempting and seems logical to simply conclude that if the posttest mean is even slightly lower than the pretest mean, the hypothesis is supported. However, before we can draw this conclusion we need to assure that our results are strong enough to conclude that the difference is unlikely to simply be due to chance and that they, instead, likely reflect a real difference in the means. This is because sample means are estimates and are expected to have some sampling error; sample means are not expected to be perfect representations of population parameters under the same conditions. Slight differences in means could simply be due to sampling error. Thus, estimates of error (such as the standard deviation or standard error) must also be considered in order to determine how likely it is that the difference in the pretest and posttest sample means represent an actual difference that would be observed in population parameters under the same conditions. For this reason, estimates of error are an important part of statistical power and determining significance.

Statistical Power and Significance

Statistical power refers to how likely it is that sample data will support the hypothesis. Think of statistical power as the ability to detect that an alternative hypothesis is true if, in fact, it is true. Power is generally increased or decreased by three factors:

• the size of the sample,
• the size of the change, difference, or pattern observed in the sample data, and
• the size of the error in the relevant estimates of the changes, differences, or patterns observed.

First, if the hypothesis is true, data to support it are more likely when the sample is larger than when it is smaller. Therefore, as sample sizes increase, power also increases. Second, if the change, difference, or pattern observed in the sample is larger or clearer, it is easier to detect and is more likely to represent a difference that would occur in the population than if it is smaller. Thus, as the size of changes, differences, or patterns observed increases, power also increases. Finally, the lower the error is, the closer the observations are expected to be to the parameters of a population. Thus, as the size of error decreases, power increases. Considering these three things together, we can summarize the components that increase power as follows:

• The larger the sample size, the more closely the sample statistics are expected to represent the population.
• The larger or clearer the change, difference, or pattern observed in the sample, the more likely it is that it reflects a change, difference, or pattern in the population.
• The less error there is in the sample statistics used to assess changes, differences, or patterns, the more likely it is that they reflect changes, differences, or patterns in the population.

Obtained Values

These components that impact statistical power are interconnected. Means are estimates for which variability must be considered. Some measures of variability and error (such as standard errors) include sample sizes in their calculations. Further, the greater the sample size, the closer a sample is to being equivalent to its population size. Thus, the formulas used in inferential statistics include variations of some or all of the three components of power to yield one of several forms of obtained values. Obtained values are results that summarize data by using inferential formulas. Inferential formulas are those used to test hypotheses. These formulas and other analyses that accompany them take into account the components of power. Data are plugged into inferential formulas which yield obtained values. Those obtained values are compared to specific thresholds to assess whether the data supported or failed to support a hypothesis. Thus, obtained values can be thought of as summaries of how much power or evidence there is to support a hypothesis.

Determining Significance

Statistical significance refers to the determination that a hypothesis is likely true in the population because there is sufficient evidence in the sample to support the hypothesis. Another way to say this is that a statistically significant result occurs when the hypothesized result was observed in the sample with enough power to conclude that the observed result was unlikely to be simply due to random chance. Essentially, when an obtained value is high enough for a given situation, it represents sufficient evidence to declare a hypothesis is significantly supported.

Significance is not absolute. Instead, it is a determination that a hypothesis is likely true but not that it is proven to be true. Recall that sampling error is assumed whenever a sample is drawn and used to represent a population. Recall also that there is no guarantee that the sample will represent the population well. Thus, there is always some chance that a hypothesis is not true in the population but that it will appear to be true in the data from a sample. The stronger the evidence is in favor of the hypothesis within the sample, the more likely it is that the hypothesis is true of the population. To say it another way, the stronger the results are, the less likely it is that they would have occurred simply due to random chance rather than because they are true. Therefore, when a result matches a hypothesis and is significant, statisticians conclude that a hypothesis is likely true and, thus, is supported by the evidence. Note that statistical significance is not necessarily indicative that a result is meaningful or useful. Instead, statistical significance simply indicates that the hypothesis is likely true based on the evidence.

Critical Values

Obtained values are compared to critical values to determine whether a hypothesis has enough evidence to be declared significant and, thus, supported. Critical values represent thresholds of the minimum amount of evidence that is needed to determine statistical significance and conclude that a hypothesis is supported. Thus, when the obtained value (which represents the amount of evidence) exceeds the critical value (which represents the minimum amount of evidence needed to support a hypothesis), the conclusion is that the hypothesis is supported. Conversely, when the obtained value does not exceed the critical value, the conclusion is that there is insufficient evidence to support the hypothesis. Another way to say this is that the null hypothesis is rejected when the obtained value exceeds the critical value and is retained or accepted when the obtained value does not exceed the critical value.

Obtained and critical values depend on several things which can include whether or not a hypothesis is directional, which inferential formula was used, and the relevant components of power for the hypothesis and corresponding formula used. We will review the specific differences and ways both obtained values and their critical values are found in subsequent chapters. For now, it is only necessary to know that each time a hypothesis is tested, an obtained value must be found, a critical value must be found, and the two must be compared. These are important steps in the larger processes of hypothesis testing.

Steps in Hypothesis Testing

In order to test a hypothesis, these steps should be followed, in the recommended order:

1. State the hypothesis.

This is a necessary first step. Before a study can be designed, a researcher needs to specify exactly what the hypothesis is what they intend to test. Then the process for collecting data (which is the research method) can be developed and carried out, accordingly.

It is worth noting that it is possible to develop a hypothesis after data have been collected but this is not ideal as it introduces important limitations to the research process. Though these limitations are beyond the scope of this book, they are an important topic which is generally covered in a Research Methods course. The focus of this book is best practices for statistical analysis; in keeping, we will always presume a hypothesis was developed before data were collected to test it. Thus, the first step for our analyses and reporting our results will always include stating the hypothesis.

2. Choose the inferential test (formula) that best fits the hypothesis.

There are a variety of formulas, each of which best fits only certain kinds of data and, thus, each only fits certain hypotheses. For example, one test is used to compare the means of the same group at posttest to itself at pretest, a different one is used to compare the mean of one group to the mean of a different group, another is used to compare the means of three or more distinct groups, and still others are used to assess patterns between two or more quantitative variables. The test selected should be the one that is best suited to the hypothesis under investigation. Note: A brief summary of the different kinds of inferential tests included in this book appears towards the end of this chapter.

3. Determine the critical value.

The critical value refers to the number you must surpass in order to conclude that your results are unlikely to be due to chance and, thus, likely reflects a truth about the population. The critical value is a concept we will discuss in more detail in subsequent chapters. For now, know that we weigh the implications of an inaccurate conclusion (e.g. what are the risks of concluding our medication worked when it actually did not) and then set the statistical risk we are willing to take that we might be wrong (which is used to determine the critical value). In the behavioral sciences, we very often decide that we are willing to accept less than a 5% chance that we will conclude a hypothesis is true when it is not; this means we want less than a 5% chance that our result is simply a false positive. Thus, critical values are usually computed to represent the amount (or strength) of evidence that is needed to be at least 95% confident that the hypothesis is true.

4. Calculate the test statistic.

This is the step of the scientific method (and, thus, also in the process of hypothesis testing) in which data are analyzed. In this step, the statistician uses the inferential test that was chosen in step 2 to analyze the data and yield a result. The result is represented by the obtained value (which is also known as a test statistic or result). This is the most math-intensive step of testing a hypothesis.

5. Apply a decision rule and determine whether the result is significant.

In this step, we assess whether our result (i.e. our obtained value or test statistic) exceeds the critical value. When it does, we can conclude that there is a strong probability that the hypothesis is true in the population based on the evidence observed in the sample. In so doing, the result is concluded to be significant. Conversely, when the test statistics does not exceed the critical value, we conclude that the evidence is not strong enough to conclude that the hypothesis is likely true in the population and, thus, that the hypothesis is not supported. In so doing, the result is concluded to be non-significant.

When a result is close to exceeding the critical value but does not, it may be prudent for researchers to retest the hypothesis or similar hypotheses with new samples in the future. A result which is close to, but does not surpass, the critical value may be referred to as “trending”; however, trending results should not be referred to as significant.

When it is determined that the result is significant, proceed through each of the remaining steps. When it is determined that the result is not significant, skip to step 7 to complete the process of hypothesis testing.

6. Calculate the effect size and other relevant secondary analyses.

An effect size can be reported alongside a significant result. Essentially, an effect size is an estimate of the magnitude of an effect, change, or pattern observed in the sample data. Effect size can help statisticians and audiences deduce practical significance. Practical significance refers to whether there is a large enough magnitude of effect to be meaningful or useful. This is important because it is possible to have a result that is statistically significant without being practically significant. Thus, it is often recommended that practical significance be reported as a secondary analysis when a result is statistically significant.

Some tests have additional secondary analyses which are necessary to adequately test a hypothesis. In each chapter for which these are recommended, they will be included in the section for step 6 of hypothesis testing.

7. Report the results in American Psychological Associate (APA) format.

Results for inferential tests are often best summarized using a paragraph that states the following:

• The hypothesis and specific inferential test used,
• The main results of the test and whether they were significant,
• Any additional results that clarify or add details about the results, and
• Whether the results support or refute the hypothesis.

It is recommended that effect sizes be reported with the additional results, when possible and/or common in the field into which the researcher is disseminating results. Dissemination refers to the formal sharing of results which is often done through publishing peer-reviewed, empirical articles in research or academic journals, giving conference presentations, and/ors reporting results in books that focus on summarizing several empirical studies. APA format specifies the level of rounding and types of symbols which should be used when reporting results for each of the various descriptive and inferential tests.

We will employ these steps when we learn how to select and properly use inferential statistics to test hypotheses in the subsequent chapters of this book. In each of those chapters, the details of the formulas, the calculations they require, and the symbols and rounding rules will be covered in detail. It will likely be helpful to refer back to this section with each of those chapters to remind yourself of the order and purpose of each of these steps to testing a hypothesis and reporting the results.

Reading Review 6.3

• In which step of hypothesis testing are data analyzed?
• What does statistical significance mean?
• Which two values are compared to determine whether a result is statistically significant?
• What is used to estimate practical significance?

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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