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13 Different Types of Hypothesis

hypothesis definition and example, explained below

There are 13 different types of hypothesis. These include simple, complex, null, alternative, composite, directional, non-directional, logical, empirical, statistical, associative, exact, and inexact.

A hypothesis can be categorized into one or more of these types. However, some are mutually exclusive and opposites. Simple and complex hypotheses are mutually exclusive, as are direction and non-direction, and null and alternative hypotheses.

Below I explain each hypothesis in simple terms for absolute beginners. These definitions may be too simple for some, but they’re designed to be clear introductions to the terms to help people wrap their heads around the concepts early on in their education about research methods .

Types of Hypothesis

Before you Proceed: Dependent vs Independent Variables

A research study and its hypotheses generally examine the relationships between independent and dependent variables – so you need to know these two concepts:

  • The independent variable is the variable that is causing a change.
  • The dependent variable is the variable the is affected by the change. This is the variable being tested.

Read my full article on dependent vs independent variables for more examples.

Example: Eating carrots (independent variable) improves eyesight (dependent variable).

1. Simple Hypothesis

A simple hypothesis is a hypothesis that predicts a correlation between two test variables: an independent and a dependent variable.

This is the easiest and most straightforward type of hypothesis. You simply need to state an expected correlation between the dependant variable and the independent variable.

You do not need to predict causation (see: directional hypothesis). All you would need to do is prove that the two variables are linked.

Simple Hypothesis Examples

2. complex hypothesis.

A complex hypothesis is a hypothesis that contains multiple variables, making the hypothesis more specific but also harder to prove.

You can have multiple independent and dependant variables in this hypothesis.

Complex Hypothesis Example

In the above example, we have multiple independent and dependent variables:

  • Independent variables: Age and weight.
  • Dependent variables: diabetes and heart disease.

Because there are multiple variables, this study is a lot more complex than a simple hypothesis. It quickly gets much more difficult to prove these hypotheses. This is why undergraduate and first-time researchers are usually encouraged to use simple hypotheses.

3. Null Hypothesis

A null hypothesis will predict that there will be no significant relationship between the two test variables.

For example, you can say that “The study will show that there is no correlation between marriage and happiness.”

A good way to think about a null hypothesis is to think of it in the same way as “innocent until proven guilty”[1]. Unless you can come up with evidence otherwise, your null hypothesis will stand.

A null hypothesis may also highlight that a correlation will be inconclusive . This means that you can predict that the study will not be able to confirm your results one way or the other. For example, you can say “It is predicted that the study will be unable to confirm a correlation between the two variables due to foreseeable interference by a third variable .”

Beware that an inconclusive null hypothesis may be questioned by your teacher. Why would you conduct a test that you predict will not provide a clear result? Perhaps you should take a closer look at your methodology and re-examine it. Nevertheless, inconclusive null hypotheses can sometimes have merit.

Null Hypothesis Examples

4. alternative hypothesis.

An alternative hypothesis is a hypothesis that is anything other than the null hypothesis. It will disprove the null hypothesis.

We use the symbol H A or H 1 to denote an alternative hypothesis.

The null and alternative hypotheses are usually used together. We will say the null hypothesis is the case where a relationship between two variables is non-existent. The alternative hypothesis is the case where there is a relationship between those two variables.

The following statement is always true: H 0 ≠ H A .

Let’s take the example of the hypothesis: “Does eating oatmeal before an exam impact test scores?”

We can have two hypotheses here:

  • Null hypothesis (H 0 ): “Eating oatmeal before an exam does not impact test scores.”
  • Alternative hypothesis (H A ): “Eating oatmeal before an exam does impact test scores.”

For the alternative hypothesis to be true, all we have to do is disprove the null hypothesis for the alternative hypothesis to be true. We do not need an exact prediction of how much oatmeal will impact the test scores or even if the impact is positive or negative. So long as the null hypothesis is proven to be false, then the alternative hypothesis is proven to be true.

5. Composite Hypothesis

A composite hypothesis is a hypothesis that does not predict the exact parameters, distribution, or range of the dependent variable.

Often, we would predict an exact outcome. For example: “23 year old men are on average 189cm tall.” Here, we are giving an exact parameter. So, the hypothesis is not composite.

But, often, we cannot exactly hypothesize something. We assume that something will happen, but we’re not exactly sure what. In these cases, we might say: “23 year old men are not on average 189cm tall.”

We haven’t set a distribution range or exact parameters of the average height of 23 year old men. So, we’ve introduced a composite hypothesis as opposed to an exact hypothesis.

Generally, an alternative hypothesis (discussed above) is composite because it is defined as anything except the null hypothesis. This ‘anything except’ does not define parameters or distribution, and therefore it’s an example of a composite hypothesis.

6. Directional Hypothesis

A directional hypothesis makes a prediction about the positivity or negativity of the effect of an intervention prior to the test being conducted.

Instead of being agnostic about whether the effect will be positive or negative, it nominates the effect’s directionality.

We often call this a one-tailed hypothesis (in contrast to a two-tailed or non-directional hypothesis) because, looking at a distribution graph, we’re hypothesizing that the results will lean toward one particular tail on the graph – either the positive or negative.

Directional Hypothesis Examples

7. non-directional hypothesis.

A non-directional hypothesis does not specify the predicted direction (e.g. positivity or negativity) of the effect of the independent variable on the dependent variable.

These hypotheses predict an effect, but stop short of saying what that effect will be.

A non-directional hypothesis is similar to composite and alternative hypotheses. All three types of hypothesis tend to make predictions without defining a direction. In a composite hypothesis, a specific prediction is not made (although a general direction may be indicated, so the overlap is not complete). For an alternative hypothesis, you often predict that the even will be anything but the null hypothesis, which means it could be more or less than H 0 (or in other words, non-directional).

Let’s turn the above directional hypotheses into non-directional hypotheses.

Non-Directional Hypothesis Examples

8. logical hypothesis.

A logical hypothesis is a hypothesis that cannot be tested, but has some logical basis underpinning our assumptions.

These are most commonly used in philosophy because philosophical questions are often untestable and therefore we must rely on our logic to formulate logical theories.

Usually, we would want to turn a logical hypothesis into an empirical one through testing if we got the chance. Unfortunately, we don’t always have this opportunity because the test is too complex, expensive, or simply unrealistic.

Here are some examples:

  • Before the 1980s, it was hypothesized that the Titanic came to its resting place at 41° N and 49° W, based on the time the ship sank and the ship’s presumed path across the Atlantic Ocean. However, due to the depth of the ocean, it was impossible to test. Thus, the hypothesis was simply a logical hypothesis.
  • Dinosaurs closely related to Aligators probably had green scales because Aligators have green scales. However, as they are all extinct, we can only rely on logic and not empirical data.

9. Empirical Hypothesis

An empirical hypothesis is the opposite of a logical hypothesis. It is a hypothesis that is currently being tested using scientific analysis. We can also call this a ‘working hypothesis’.

We can to separate research into two types: theoretical and empirical. Theoretical research relies on logic and thought experiments. Empirical research relies on tests that can be verified by observation and measurement.

So, an empirical hypothesis is a hypothesis that can and will be tested.

  • Raising the wage of restaurant servers increases staff retention.
  • Adding 1 lb of corn per day to cows’ diets decreases their lifespan.
  • Mushrooms grow faster at 22 degrees Celsius than 27 degrees Celsius.

Each of the above hypotheses can be tested, making them empirical rather than just logical (aka theoretical).

10. Statistical Hypothesis

A statistical hypothesis utilizes representative statistical models to draw conclusions about broader populations.

It requires the use of datasets or carefully selected representative samples so that statistical inference can be drawn across a larger dataset.

This type of research is necessary when it is impossible to assess every single possible case. Imagine, for example, if you wanted to determine if men are taller than women. You would be unable to measure the height of every man and woman on the planet. But, by conducting sufficient random samples, you would be able to predict with high probability that the results of your study would remain stable across the whole population.

You would be right in guessing that almost all quantitative research studies conducted in academic settings today involve statistical hypotheses.

Statistical Hypothesis Examples

  • Human Sex Ratio. The most famous statistical hypothesis example is that of John Arbuthnot’s sex at birth case study in 1710. Arbuthnot used birth data to determine with high statistical probability that there are more male births than female births. He called this divine providence, and to this day, his findings remain true: more men are born than women.
  • Lady Testing Tea. A 1935 study by Ronald Fisher involved testing a woman who believed she could tell whether milk was added before or after water to a cup of tea. Fisher gave her 4 cups in which one randomly had milk placed before the tea. He repeated the test 8 times. The lady was correct each time. Fisher found that she had a 1 in 70 chance of getting all 8 test correct, which is a statistically significant result.

11. Associative Hypothesis

An associative hypothesis predicts that two variables are linked but does not explore whether one variable directly impacts upon the other variable.

We commonly refer to this as “ correlation does not mean causation ”. Just because there are a lot of sick people in a hospital, it doesn’t mean that the hospital made the people sick. There is something going on there that’s causing the issue (sick people are flocking to the hospital).

So, in an associative hypothesis, you note correlation between an independent and dependent variable but do not make a prediction about how the two interact. You stop short of saying one thing causes another thing.

Associative Hypothesis Examples

  • Sick people in hospital. You could conduct a study hypothesizing that hospitals have more sick people in them than other institutions in society. However, you don’t hypothesize that the hospitals caused the sickness.
  • Lice make you healthy. In the Middle Ages, it was observed that sick people didn’t tend to have lice in their hair. The inaccurate conclusion was that lice was not only a sign of health, but that they made people healthy. In reality, there was an association here, but not causation. The fact was that lice were sensitive to body temperature and fled bodies that had fevers.

12. Causal Hypothesis

A causal hypothesis predicts that two variables are not only associated, but that changes in one variable will cause changes in another.

A causal hypothesis is harder to prove than an associative hypothesis because the cause needs to be definitively proven. This will often require repeating tests in controlled environments with the researchers making manipulations to the independent variable, or the use of control groups and placebo effects .

If we were to take the above example of lice in the hair of sick people, researchers would have to put lice in sick people’s hair and see if it made those people healthier. Researchers would likely observe that the lice would flee the hair, but the sickness would remain, leading to a finding of association but not causation.

Causal Hypothesis Examples

13. exact vs. inexact hypothesis.

For brevity’s sake, I have paired these two hypotheses into the one point. The reality is that we’ve already seen both of these types of hypotheses at play already.

An exact hypothesis (also known as a point hypothesis) specifies a specific prediction whereas an inexact hypothesis assumes a range of possible values without giving an exact outcome. As Helwig [2] argues:

“An “exact” hypothesis specifies the exact value(s) of the parameter(s) of interest, whereas an “inexact” hypothesis specifies a range of possible values for the parameter(s) of interest.”

Generally, a null hypothesis is an exact hypothesis whereas alternative, composite, directional, and non-directional hypotheses are all inexact.

See Next: 15 Hypothesis Examples

This is introductory information that is basic and indeed quite simplified for absolute beginners. It’s worth doing further independent research to get deeper knowledge of research methods and how to conduct an effective research study. And if you’re in education studies, don’t miss out on my list of the best education studies dissertation ideas .

[1] https://jnnp.bmj.com/content/91/6/571.abstract

[2] http://users.stat.umn.edu/~helwig/notes/SignificanceTesting.pdf

Chris

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

  • Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 41 Important Classroom Expectations (for This School Year)
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2 thoughts on “13 Different Types of Hypothesis”

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Wow! This introductionary materials are very helpful. I teach the begginers in research for the first time in my career. The given tips and materials are very helpful. Chris, thank you so much! Excellent materials!

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You’re more than welcome! If you want a pdf version of this article to provide for your students to use as a weekly reading on in-class discussion prompt for seminars, just drop me an email in the Contact form and I’ll get one sent out to you.

When I’ve taught this seminar, I’ve put my students into groups, cut these definitions into strips, and handed them out to the groups. Then I get them to try to come up with hypotheses that fit into each ‘type’. You can either just rotate hypothesis types so they get a chance at creating a hypothesis of each type, or get them to “teach” their hypothesis type and examples to the class at the end of the seminar.

Cheers, Chris

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Educator, Researcher

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

  • A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
  • It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
  • A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
  • Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
  • For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
  • Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.
Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

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

Deeptanshu D

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

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-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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  • How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

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

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

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

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

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

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

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

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

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

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

 Statistics

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

Research bias

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

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

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

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

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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."

types of hypothesis

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.

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  • 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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What Are Examples of a Hypothesis?

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A hypothesis is an explanation for a set of observations. Here are examples of a scientific hypothesis.

Although you could state a scientific hypothesis in various ways, most hypotheses are either "If, then" statements or forms of the null hypothesis . The null hypothesis is sometimes called the "no difference" hypothesis. The null hypothesis is good for experimentation because it's simple to disprove. If you disprove a null hypothesis, that is evidence for a relationship between the variables you are examining.

Examples of Null Hypotheses

  • Hyperactivity is unrelated to eating sugar.
  • All daisies have the same number of petals.
  • The number of pets in a household is unrelated to the number of people living in it.
  • A person's preference for a shirt is unrelated to its color.

Examples of If, Then Hypotheses

  • If you get at least 6 hours of sleep, you will do better on tests than if you get less sleep.
  • If you drop a ball, it will fall toward the ground.
  • If you drink coffee before going to bed, then it will take longer to fall asleep.
  • If you cover a wound with a bandage, then it will heal with less scarring.

Improving a Hypothesis to Make It Testable

You may wish to revise your first hypothesis in order to make it easier to design an experiment to test. For example, let's say you have a bad breakout the morning after eating a lot of greasy food. You may wonder if there is a correlation between eating greasy food and getting pimples. You propose the hypothesis:

Eating greasy food causes pimples.

Next, you need to design an experiment to test this hypothesis. Let's say you decide to eat greasy food every day for a week and record the effect on your face. Then, as a control, you'll avoid greasy food for the next week and see what happens. Now, this is not a good experiment because it does not take into account other factors such as hormone levels, stress, sun exposure, exercise, or any number of other variables that might conceivably affect your skin.

The problem is that you cannot assign cause to your effect . If you eat french fries for a week and suffer a breakout, can you definitely say it was the grease in the food that caused it? Maybe it was the salt. Maybe it was the potato. Maybe it was unrelated to diet. You can't prove your hypothesis. It's much easier to disprove a hypothesis.

So, let's restate the hypothesis to make it easier to evaluate the data:

Getting pimples is unaffected by eating greasy food.

So, if you eat fatty food every day for a week and suffer breakouts and then don't break out the week that you avoid greasy food, you can be pretty sure something is up. Can you disprove the hypothesis? Probably not, since it is so hard to assign cause and effect. However, you can make a strong case that there is some relationship between diet and acne.

If your skin stays clear for the entire test, you may decide to accept your hypothesis . Again, you didn't prove or disprove anything, which is fine

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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

What is a Hypothesis

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

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Nayturr

8 Different Types of Hypotheses (Plus Essential Facts)

A hand highlighting the word

The hypothesis is an idea or a premise used as a jumping off the ground for further investigation. It’s essential to scientific research because it serves as a compass for scientists or researchers in carrying out their experiments or studies.

There are different types of hypotheses but crafting a good hypothesis can be tricky. A sound hypothesis should be logical, affirmative, clear, precise, quantifiable, or can be tested, and has a cause and effect factor.

Types 

Alternative hypothesis.

Also known as a maintained hypothesis or a research hypothesis, an alternative hypothesis is the exact opposite of a null hypothesis, and it is often used in statistical hypothesis testing. There are four main types of alternative hypothesis:

  • Point alternative hypothesis . This hypothesis occurs when the population distribution in the hypothesis test is fully defined and has no unknown parameters. It usually has no practical interest, but it is considered important in other statistical activities.
  • Non-directional alternative hypothesis. These hypotheses have nothing to do with the either region of rejection (i.e., one-tailed or two-tailed directional hypotheses) but instead, only that the null hypothesis is untrue.
  • One-tailed directional hypothesis. This hypothesis is only concerned with the region of direction for one tail of a sampling distribution, not both of them.
  • Two-tailed directional hypothesis. This hypothesis is concerned with both regions of rejection of a particular sampling distribution

Known by the symbol H1, this type of hypothesis proclaims the expected relationship between the variables in the theory.

Associative and Causal Hypothesis

Associative hypotheses simply state that there is a relationship between two variables, whereas causal hypotheses state that any difference in the type or amount of one particular variable is going to directly affect the difference in the type or amount of the next variable in the equation.

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These hypotheses are often used in the field of psychology. A causal hypothesis looks at how manipulation affects events in the future, while an associative hypothesis looks at how specific events co-occur.

A good example of its practical use occurs when discussing the psychological aspects of eyewitness testimonies, and they generally affect four areas of this phenomenon: emotion and memory, system variables in the line-up, estimation of the duration of the event, and own-race bias.

Complex Hypothesis

In a complex hypothesis, a relationship exists between the variables . In these hypotheses, there are more than two independent and dependent variables, as demonstrated in the following hypotheses:

  • Taking drugs and smoking cigarettes leads to respiratory problems, increased tension, and cancer.
  • The people who are older and living in rural areas are happier than people who are younger and who live in the city or suburbs.
  • If you eat a high-fat diet and a few vegetables, you are more likely to suffer from hypertension and high cholesterol than someone who eats a lot of vegetables and sticks to a low-fat diet.

Directional Hypothesis

A directional hypothesis is one regarding either a positive or negative difference or change in the two variables involved. Typically based on aspects such as accepted theory, literature printed on the topic at hand, past research, and even accepted theory, researchers normally develop this type of hypothesis from research questions, and they use statistical methods to check its validity.

Words you often hear in hypotheses that are directional in nature include more, less, increase, decrease, positive, negative, higher, and lower. Directional hypotheses specify the direction or nature of the relationship between two or more independent variables and two or more dependent variables.

Non-Directional Hypothesis

This hypothesis states that there is a distinct relationship between two variables; however, it does not predict the exact nature or direction of that particular relationship.

Null Hypothesis

Null hypothesis with gear icons as background.

Indicated by the symbol Ho, a null hypothesis predicts that the variables in a certain hypothesis have no relationship to one another and that the hypothesis is normally subjected to some type of statistical analysis. It essentially states that the data and variables being investigated do not actually exist.

A perfect example of this comes when looking at scientific medical studies, where you have both an experimental and control group, and you are hypothesizing that there will be no difference in the results of these two groups.

Simple Hypothesis

This hypothesis consists of two variables, an independent variable or cause, and a dependent variable or cause. Simple hypotheses contain a relationship between these two variables. For example, the following are examples of simple hypotheses:

  • The more you chew tobacco, the more likely you are to develop mouth cancer.
  • The more money you make, the less likely you are to be involved in criminal activity.
  • The more educated you are, the more likely you are to have a well-paying job.

Statistical Hypothesis

This is just a hypothesis that is able to be verified through statistics. It can be either logical or illogical, but if you can use statistics to verify it, it is called a statistical hypothesis.

Facts about Hypotheses

types of hypothesis

Difference Between Simple and Complex Hypotheses

In a simple hypothesis, there is a dependent and an independent variable, as well as a relationship between the two. The independent variable is the cause and comes first when they’re in chronological order, and the dependent variable describes the effect. In a complex hypothesis, the relationship is between two or more independent variables and two or more dependent variables.

Difference Between Non-Directional and Directional Hypotheses

In a directional research hypothesis, the direction of the relationship is predicted. The advantages of this type of hypothesis include one-tailed statistical tests, theoretical propositions that can be tested in a more precise manner, and the fact that the researcher’s expectations are very clear right from the start.

In a non-directional research hypothesis, the relationship between the variables is predicted but not the direction of that relationship. Reasons to use this type of research hypothesis include when your previous research findings contradict one another and when there is no theory on which to base your predictions.

Difference Between a Hypothesis and a Theory

There are many different differences between a theory and a hypothesis, including the following:

  • A hypothesis is a suggestion of what might happen when you test out a theory. It is a prediction of a possible correlation between various phenomena. On the other hand, a theory has been tested and is well-substantiated. If a hypothesis succeeds in proving a certain point, it can then be called a theory.
  • The data for a hypothesis is most often very limited, whereas the data relating to theory has been tested under numerous circumstances.
  • A hypothesis offers a very specific instance; that is, it is limited to just one observation. On the other hand, a theory is more generalized and is put through a multitude of experiments and tests, which can then apply to various specific instances.
  • The purposes of these two items are different as well. A hypothesis starts with a possibility that is uncertain but can be studied further via observations and experiments. A theory is used to explain why large sets of observations are continuously made.
  • Hypotheses are based on various suggestions and possibilities but have uncertain results, while theories have a steady and reliable consensus among scientists and other professionals.
  • Both theories and hypotheses are testable and falsifiable, but unlike theories, hypotheses are neither well-tested nor well-substantiated.

What is the Interaction Effect?

This effect describes the two variables’ relationship to one another.

When Writing the Hypothesis, There is a Certain Format to Follow

This includes three aspects:

  • The correlational statement
  • The comparative statement
  • A statistical analysis

How are Hypotheses Used to Test Theories?

  • Do not test the entire theory, just the proposition
  • It can never be either proved or disproved

When Formulating a Hypothesis, There are Things to Consider

These include:

  • You have to write it in the present tense
  • It has to be empirically testable
  • You have to write it in a declarative sentence
  • It has to contain all of the variables
  • It must contain three parts: the purpose statement, the problem statement, and the research question
  • It has to contain the population

What is the Best Definition of a Scientific Hypothesis?

It is essentially an educated guess; however, that guess will lose its credibility if it is falsifiable.

How to Use Research Questions

There are two ways to include research questions when testing a theory. The first is in addition to a hypothesis related to the topic’s other areas of interest, and the second is in place of the actual hypothesis, which occurs in some instances.

Tips to Keep in Mind When Developing a Hypothesis

  • Use language that is very precise. Your language should be concise, simple, and clean. This is not a time when you want to be vague, because everything needs to be spelled out in great detail.
  • Be as logical as possible. If you believe in something, you want to prove it, and remaining logical at all times is a great start.
  • Use research and experimentation to determine whether your hypothesis is testable. All hypotheses need to be proven. You have to know that proving your theory is going to work, even if you find out different in the end.

What is the Number-One Purpose of a Scientific Method?

Scientific methods are there to provide a structured way to get the appropriate evidence in order to either refute or prove a scientific hypothesis.

Glossary of Terms Related to Hypotheses

Scientist pointing on a chalkboard to explain the scientific method steps.

Bivariate Data: This is data that includes two distinct variables, which are random and usually graphed via a scatter plot.

Categorical Data: These data fit into a tiny number of very discrete categories. They are usually either nominal, or non-ordered, which can include things such as age or country; or they can be ordinal, or ordered, which includes aspects such as hot or cold temperature.

Correlation: This is a measure of how closely two variables are to one another. It measures whether a change in one random variable corresponds to a change in the other random variable. For example, the correlation between smoking and getting lung cancer has been widely studied.

Data: These are the results found from conducting a survey or experiment, or even an observation study of some type.

Dependent Event: If the happening of one event affects the probability of another event occurring also, they are said to be dependent events.

Distribution: The way the probability of a random variable taking a certain value is described is called its distribution. Possible distribution functions include the cumulative, probability density, or probability mass function.

Element: This refers to an object in a certain set, and that object is an element of that set.

Empirical Probability: This refers to the likelihood of an outcome happening, and it is determined by the repeat performance of a particular experiment.  You can do this by dividing the number of times that event took place by the number of times you conducted the experiment.

Equality of Sets: If two sets contain the exact same elements, they are considered equal sets. In order to determine if this is so, it can be advantageous to show that each set is contained in the other set.

Equally Likely Outcomes: Refers to outcomes that have the same probability; for example, if you toss a coin there are only two likely outcomes.

Event: This term refers to the subset of a sample space.

Expected Value: This demonstrates the average value of a quantity that is random and which has been observed numerous times in order to duplicate the same results of previous experiments.

Experiment: A scientific process that results in a set of outcomes that is observable. Even selecting a toy from a box of toys can be considered an experiment in this instance.

Experimental Probability: When you estimate how likely something is to occur, this is an experimental probability example. To get this probability, you divide the number of trials that were successful by the total number of trials that were performed.

Finite Sample Space: These sample spaces have a finite number of outcomes that could possibly occur.

Frequency: The frequency is the number of times a certain value occurs when you observe an experiment’s results.

Frequency Distribution: This refers to the data that describes possible groups or values and the frequencies that correspond to those groups or values.

Histogram: A histogram, or frequency histogram, is a bar graph that demonstrates how frequently data points occur.

Independent Event: If two events occur, and one event’s outcome has no effect on the other’s outcome, this is known as an independent event.

Infinite Sample Space: This refers to a sample space that consists of outcomes with an infinite number of possibilities.

Mutually Exclusive: Events are mutually exclusive if their outcomes have absolutely nothing in common.

Notations: Notations are operations or quantities described by symbols instead of numbers.

Observational Study: Like the name implies, these are studies that allow you to collect data through basic observation.

Odds: This is a way to express the likelihood that a certain event will happen. If you see odds of m:n, it means it is expected that a certain event will happen m times for every n times it does not happen.

One-Variable Data: Data that have related behaviors usually associated in some important way.

Outcome: The outcome is simply the result of a particular experiment. If you consider a set of all of the possible outcomes, this is called the sample space.

Probability: A probability is merely the likelihood that a certain event will take place, and it is expressed on a scale of 0 to one, with 0 meaning it is impossible that it will happen and one being a certainty that it will happen. Probability can also be expressed as a percentage, starting with 0 and ending at 100%.

Random Experiment: A random experiment is one whereby the outcome can’t be predicted with any amount of certainty, at least not before the experiment actually takes place.

Random Variable: Random variables take on different numerical values, based on the results of a particular experiment.

Replacement: Replacement is the act of returning or replacing an item back into a sample space, which takes place after an event and allows the item to be chosen more than one time.

Sample Space: This term refers to all of the possible outcomes that could result from a probability experiment.

Set: A collection of objects that is well-defined is called a set.

Simple Event: When an event is a single element of the sample space, it is known as a simple event.

Simulation: A simulation is a type of experiment that mimics a real-life event.

Single-Variable Data: These are data that use only one unknown variable.

Statistics: This is the branch of mathematics that deals with the study of quantitative data. If you analyze certain events that are governed by probability, this is called statistics.

Theoretical Probability: This probability describes the ratio of the number of outcomes in a specific event to the number of outcomes found in the sample space. It is based on the presumption that all outcomes are equally liable.

Union: Usually described by the symbol ∪, or the cup symbol, a union describes the combination of two or more sets and their elements.

Variable: A variable is a quantity that varies and is almost always represented by letters.

8 different types of hypotheses.

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How to Develop a Good Research Hypothesis

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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).

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Essential 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?

research hypothesis example

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 an effective 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

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.

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

Thanks a lot for your valuable guidance.

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

Useful piece!

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

Nicely explained

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

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

clear and concise. thanks.

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Good to learn

Thanks a lot for explaining to my level of understanding

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What is Hypothesis? Definition, Meaning, Characteristics, Sources

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

What is Hypothesis?

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

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

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

Table of Content

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

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

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

Hypothesis Definition

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

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

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

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

Meaning of Hypothesis

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

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

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

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

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

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

Characteristics of Hypothesis

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

Conceptual Clarity

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

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

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

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

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

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

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

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

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

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

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

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

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

Sources of Hypothesis

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

Observation

State of knowledge, continuity of research.

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

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

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

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

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

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

Null and Alternative Hypothesis

Null hypothesis.

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

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

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

Alternative Hypothesis

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

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

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Hypothesis: Functions, Problems, Types, Characteristics, Examples

Basic Elements of the Scientific Method: Hypotheses

The Function of the Hypotheses

A hypothesis states what one is looking for in an experiment. When facts are assembled, ordered, and seen in a relationship, they build up to become a theory. This theory needs to be deduced for further confirmation of the facts, this formulation of the deductions constitutes of a hypothesis. As a theory states a logical relationship between facts and from this, the propositions which are deduced should be true. Hence, these deduced prepositions are called hypotheses.

Problems in Formulating the Hypothesis

As difficult as the process may be, it is very essential to understand the need of a hypothesis. The research would be much unfocused and a random empirical wandering without it. The hypothesis provides a necessary link between the theory and investigation which often leads to the discovery of additions to knowledge.

There are three major difficulties in the formulation of a hypothesis, they are as follows:

  • Absence of a clear theoretical framework
  • Lack of ability to utilize that theoretical framework logically
  • Failure to be acquainted with available research techniques so as to phrase the hypothesis properly.

Sometimes the deduction of a hypothesis may be difficult as there would be many variables and the necessity to take them all into consideration becomes a challenge. For instance, observing two cases:

  • Principle: A socially recognized relationship with built-in strains also governed by the institutional controls has to ensure conformity of the participants with implicit or explicit norms.

Deduction: This situation holds much more sense to the people who are in professions such as psychotherapy, psychiatry and law to some extent. They possess a very intimate relationship with their clients, thus are more susceptible to issues regarding emotional strains in the client-practitioner relationship and more implicit and explicit controls over both participants in comparison to other professions.

The above-mentioned case has variable hypotheses, so the need is to break them down into sub hypotheses, they are as follows:

  • Specification of the degree of difference
  • Specification of profession and problem
  • Specification of kinds of controls.

2. Principle: Extensive but relatively systematized data show the correlation between members of the upper occupational class and less unhappiness and worry. Also, they are subjected to more formal controls than members of the lower strata.

Deduction: There can numerous ways to approach this principle, one could go with the comparison applying to martial relationships of the members and further argue that such differential pressures could be observed through divorce rates. This hypothesis would show inverse correlations between class position and divorce rates. There would be a very strong need to define the terms carefully to show the deduction from the principle problem.

The reference of these examples showcases a major issue in the hypothesis formulations procedures. One needs to keep the lines set for the deductions and one should be focusing on having a hypothesis at the beginning of the experiment, that hypothesis may be subject to change in the later stages and it is referred to as a „working hypothesis. Hence, the devising and utilization of a hypothesis is essential for the success of the experiment.

Types of Hypothesis

There are many ways to classify hypotheses, but it seems adequate to distinguish to separate them on the basis of their level of abstraction. They can be divided into three broad levels which will be increasing in abstractness.

  • The existence of empirical uniformities : These hypotheses are made from problems which usually have a very high percentage of representing scientific examination of common–sense proportions. These studies may show a variety of things such as the distribution of business establishments in a city, behavior patterns of specific groups, etc. and they tend to show no irregularities in their data collection or review. There have been arguments which say that these aren’t hypothesis as they represent what everyone knows. This can be counter argued on the basis of two things that, “what everyone knows” isn’t always in coherence with the framework of science and it may also be incorrect. Hence, testing these hypotheses is necessary too.
  • Complex ideal types: These hypotheses aim at testing the existence of logically derived relationships between empirical uniformities. This can be understood with an example, to observe ecology one should take in many factors and see the relationship between and how they affect the greater issue. A theory by Ernest W. Burgess gave out the statement that concentric growth circles are the one which characterize the city. Hence, all issues such as land values, industrial growth, ethnic groups, etc. are needed to be analyzed for forming a correct and reasonable hypothesis.
  • Relations of analytic variables: These hypotheses are a bit more complex as they focus on they lead to the formulation of a relationship between the changes in one property with respect to another. For instance, taking the example of human fertility in diverse regions, religions, wealth gap, etc. may not always affect the end result but it doesn’t mean that the variables need not be accounted for. This level of hypothesizing is one of the most effective and sophisticated and thus is only limited by theory itself.

Science and Hypothesis

“The general culture in which a science develops furnishes many of its basic hypotheses” holds true as science has developed more in the West and is no accident that it is a function of culture itself. This is quite evident with the culture of the West as they read for morals, science and happiness. After the examination of a bunch of variables, it is quite easy to say that the cultural emphasis upon happiness has been productive of an almost limitless range.

The hypotheses originate from science; a key example in the form of “socialization” may be taken. The socialization process in learning science involves a feedback mechanism between the scientist and the student. The student learns from the scientist and then tests for results with his own experience, and the scientist in turn has to do the same with his colleagues.

Analogies are a source of useful hypotheses but not without its dangers as all variables may not be accounted for it as no civilization has a perfect system.

Hypotheses are also the consequence of personal, idiosyncratic experience as the manner in which the individual reacts to the hypotheses is also important and should be accounted for in the experiment.

The Characteristics for Usable Hypotheses

The criteria for judging a hypothesis as mentioned below:

  • Complete Clarity : A good hypothesis should have two main elements, the concepts should be clearly defined and they should be definitions which are communicable and accepted by a larger section of the public. A lot of sources may be used and fellow associates may be used to help with the cause.
  • Empirical Referents : A great hypothesis should have scientific concepts with the ultimate empirical referent. It can‟t be based on moral judgment though it can explore them but the goal should be separated from moral preachment and the acceptance of values. A good start could be analyzing the concepts which express attitudes rather than describing or referring to empirical phenomena.
  • Specific Goal : The goal and procedure of the hypothesis should be tangible as grand experiments are harder to carry out. All operations and predictions should be mapped and in turn the possibility of testing the hypothesis increases. This not only enables the conceptual clarity but also the description of any indexes used. These indexes are used as variables for testing hypotheses on a larger scale. A general prediction isn’t as reliable as a specific prediction as the specific prediction provides a better result.
  • Relation to Available Techniques : The technique with which a hypothesis is tested is of the utmost importance and so thorough research should be carried out before the experiment in order to find the best possible way to go about it. The example of Karl Marx may be given regarding his renowned theories; he formulated his hypothesis by observing individuals and thus proving his hypothesis. So, finding the right technique may be the key to a successful test.
  • Relation to a Body of Theory: Theories on social relations can never be developed in isolation but they are a further extension of already developed or developing theories. For instance, if the “intelligence quotient” of a member of the society is to be measured, certain variables such as caste, ethnicity, nationality, etc. are chosen thus deductions are made from time to time to eventually find out what is the factor that influences intelligence.

The Conclusion

The formulation of a hypothesis is probably the most necessary step in good research practice and it is very essential to get the thought process started. It helps the researcher to have a specific goal in mind and deduce the end result of an experiment with ease and efficiency. History is evident that asking the right questions always works out fine.

Also Read: Research Methods – Basics

Goode, W. E. and P. K. Hatt. 1952. Methods in Social Research.New York: McGraw Hill. Chapters 5 and 6. Pp. 41-73

types of hypothesis

Kartik is studying BA in International Relations at Amity and Dropped out of engineering from NIT Hamirpur and he lived in over 5 different countries.

  • Math Article

Hypothesis Definition

In Statistics, the determination of the variation between the group of data due to true variation is done by hypothesis testing. The sample data are taken from the population parameter based on the assumptions. The hypothesis can be classified into various types. In this article, let us discuss the hypothesis definition, various types of hypothesis and the significance of hypothesis testing, which are explained in detail.

Hypothesis Definition in Statistics

In Statistics, a hypothesis is defined as a formal statement, which gives the explanation about the relationship between the two or more variables of the specified population. It helps the researcher to translate the given problem to a clear explanation for the outcome of the study. It clearly explains and predicts the expected outcome. It indicates the types of experimental design and directs the study of the research process.

Types of Hypothesis

The hypothesis can be broadly classified into different types. They are:

Simple Hypothesis

A simple hypothesis is a hypothesis that there exists a relationship between two variables. One is called a dependent variable, and the other is called an independent variable.

Complex Hypothesis

A complex hypothesis is used when there is a relationship between the existing variables. In this hypothesis, the dependent and independent variables are more than two.

Null Hypothesis

In the null hypothesis, there is no significant difference between the populations specified in the experiments, due to any experimental or sampling error. The null hypothesis is denoted by H 0 .

Alternative Hypothesis

In an alternative hypothesis, the simple observations are easily influenced by some random cause. It is denoted by the H a or H 1 .

Empirical Hypothesis

An empirical hypothesis is formed by the experiments and based on the evidence.

Statistical Hypothesis

In a statistical hypothesis, the statement should be logical or illogical, and the hypothesis is verified statistically.

Apart from these types of hypothesis, some other hypotheses are directional and non-directional hypothesis, associated hypothesis, casual hypothesis.

Characteristics of Hypothesis

The important characteristics of the hypothesis are:

  • The hypothesis should be short and precise
  • It should be specific
  • A hypothesis must be related to the existing body of knowledge
  • It should be capable of verification

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