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Writing Null Hypotheses in Research and Statistics
Last Updated: July 24, 2023 Fact Checked
This article was co-authored by Joseph Quinones and by wikiHow staff writer, Jennifer Mueller, JD . Joseph Quinones is a High School Physics Teacher working at South Bronx Community Charter High School. Joseph specializes in astronomy and astrophysics and is interested in science education and science outreach, currently practicing ways to make physics accessible to more students with the goal of bringing more students of color into the STEM fields. He has experience working on Astrophysics research projects at the Museum of Natural History (AMNH). Joseph recieved his Bachelor's degree in Physics from Lehman College and his Masters in Physics Education from City College of New York (CCNY). He is also a member of a network called New York City Men Teach. There are 8 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 16,045 times.
Are you working on a research project and struggling with how to write a null hypothesis? Well, you've come to the right place! Start by recognizing that the basic definition of "null" is "none" or "zero"—that's your biggest clue as to what a null hypothesis should say. Keep reading to learn everything you need to know about the null hypothesis, including how it relates to your research question and your alternative hypothesis as well as how to use it in different types of studies.
Things You Should Know
- Write a research null hypothesis as a statement that the studied variables have no relationship to each other, or that there's no difference between 2 groups.
- Adjust the format of your null hypothesis to match the statistical method you used to test it, such as using "mean" if you're comparing the mean between 2 groups.
What is a null hypothesis?
- Research hypothesis: States in plain language that there's no relationship between the 2 variables or there's no difference between the 2 groups being studied.
- Statistical hypothesis: States the predicted outcome of statistical analysis through a mathematical equation related to the statistical method you're using.
Examples of Null Hypotheses
Null Hypothesis vs. Alternative Hypothesis
- For example, your alternative hypothesis could state a positive correlation between 2 variables while your null hypothesis states there's no relationship. If there's a negative correlation, then both hypotheses are false.
- You need additional data or evidence to show that your alternative hypothesis is correct—proving the null hypothesis false is just the first step.
- In smaller studies, sometimes it's enough to show that there's some relationship and your hypothesis could be correct—you can leave the additional proof as an open question for other researchers to tackle.
How do I test a null hypothesis?
- Group means: Compare the mean of the variable in your sample with the mean of the variable in the general population.  X Research source
- Group proportions: Compare the proportion of the variable in your sample with the proportion of the variable in the general population.  X Research source
- Correlation: Correlation analysis looks at the relationship between 2 variables—specifically, whether they tend to happen together.  X Research source
- Regression: Regression analysis reveals the correlation between 2 variables while also controlling for the effect of other, interrelated variables.  X Research source
Templates for Null Hypotheses
- Research null hypothesis: There is no difference in the mean [dependent variable] between [group 1] and [group 2].
- Research null hypothesis: The proportion of [dependent variable] in [group 1] and [group 2] is the same.
- Research null hypothesis: There is no correlation between [independent variable] and [dependent variable] in the population.
- Research null hypothesis: There is no relationship between [independent variable] and [dependent variable] in the population.
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Thanks for reading our article! If you’d like to learn more about physics, check out our in-depth interview with Joseph Quinones .
- ↑ https://www.collin.edu/studentresources/tutoring/What%20is%20a%20hypothesis.pdf
- ↑ https://online.stat.psu.edu/stat100/lesson/10/10.1
- ↑ https://online.stat.psu.edu/stat501/lesson/2/2.12
- ↑ https://support.minitab.com/en-us/minitab/21/help-and-how-to/statistics/basic-statistics/supporting-topics/basics/null-and-alternative-hypotheses/
- ↑ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635437/
- ↑ https://online.stat.psu.edu/statprogram/reviews/statistical-concepts/hypothesis-testing
- ↑ https://education.arcus.chop.edu/null-hypothesis-testing/
- ↑ https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_hypothesistest-means-proportions/bs704_hypothesistest-means-proportions_print.html
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- Null and Alternative Hypotheses | Definitions & Examples
Null and Alternative Hypotheses | Definitions & Examples
Published on 5 October 2022 by Shaun Turney . Revised on 6 December 2022.
The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test :
- Null hypothesis (H 0 ): There’s no effect in the population .
- Alternative hypothesis (H A ): There’s an effect in the population.
The effect is usually the effect of the independent variable on the dependent variable .
Table of contents
Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, differences between null and alternative hypotheses, how to write null and alternative hypotheses, frequently asked questions about null and alternative hypotheses.
The null and alternative hypotheses offer competing answers to your research question . When the research question asks “Does the independent variable affect the dependent variable?”, the null hypothesis (H 0 ) answers “No, there’s no effect in the population.” On the other hand, the alternative hypothesis (H A ) answers “Yes, there is an effect in the population.”
The null and alternative are always claims about the population. That’s because the goal of hypothesis testing is to make inferences about a population based on a sample . Often, we infer whether there’s an effect in the population by looking at differences between groups or relationships between variables in the sample.
You can use a statistical test to decide whether the evidence favors the null or alternative hypothesis. Each type of statistical test comes with a specific way of phrasing the null and alternative hypothesis. However, the hypotheses can also be phrased in a general way that applies to any test.
The null hypothesis is the claim that there’s no effect in the population.
If the sample provides enough evidence against the claim that there’s no effect in the population ( p ≤ α), then we can reject the null hypothesis . Otherwise, we fail to reject the null hypothesis.
Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept. Be careful not to say you “prove” or “accept” the null hypothesis.
Null hypotheses often include phrases such as “no effect”, “no difference”, or “no relationship”. When written in mathematical terms, they always include an equality (usually =, but sometimes ≥ or ≤).
Examples of null hypotheses
The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started.
*Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p 1 = p 2 .
The alternative hypothesis (H A ) is the other answer to your research question . It claims that there’s an effect in the population.
Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true.
The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time.
Alternative hypotheses often include phrases such as “an effect”, “a difference”, or “a relationship”. When alternative hypotheses are written in mathematical terms, they always include an inequality (usually ≠, but sometimes > or <). As with null hypotheses, there are many acceptable ways to phrase an alternative hypothesis.
Examples of alternative hypotheses
The table below gives examples of research questions and alternative hypotheses to help you get started with formulating your own.
Null and alternative hypotheses are similar in some ways:
- They’re both answers to the research question
- They both make claims about the population
- They’re both evaluated by statistical tests.
However, there are important differences between the two types of hypotheses, summarized in the following table.
To help you write your hypotheses, you can use the template sentences below. If you know which statistical test you’re going to use, you can use the test-specific template sentences. Otherwise, you can use the general template sentences.
The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:
Does independent variable affect dependent variable ?
- Null hypothesis (H 0 ): Independent variable does not affect dependent variable .
- Alternative hypothesis (H A ): Independent variable affects dependent variable .
Once you know the statistical test you’ll be using, you can write your hypotheses in a more precise and mathematical way specific to the test you chose. The table below provides template sentences for common statistical tests.
Note: The template sentences above assume that you’re performing one-tailed tests . One-tailed tests are appropriate for most studies.
The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).
The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).
A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).
A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.
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- How to Write a Strong Hypothesis | Steps & Examples
How to Write a Strong Hypothesis | Steps & Examples
Published on May 6, 2022 by Shona McCombes . Revised on August 15, 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 .
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
- Null hypothesis
- Statistical power
- Probability distribution
- Effect size
- Poisson distribution
- 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 Hypothesis
If I [do something], then [this] will happen.
This basic statement/formula should be pretty familiar to all of you as it is the starting point of almost every scientific project or paper. It is a hypothesis – a statement that showcases what you “think” will happen during an experiment. This assumption is made based on the knowledge, facts, and data you already have.
How do you write a hypothesis? If you have a clear understanding of the proper structure of a hypothesis, you should not find it too hard to create one. However, if you have never written a hypothesis before, you might find it a bit frustrating. In this article from EssayPro - custom essay writing services , we are going to tell you everything you need to know about hypotheses, their types, and practical tips for writing them.
According to the definition, a hypothesis is an assumption one makes based on existing knowledge. To elaborate, it is a statement that translates the initial research question into a logical prediction shaped on the basis of available facts and evidence. To solve a specific problem, one first needs to identify the research problem (research question), conduct initial research, and set out to answer the given question by performing experiments and observing their outcomes. However, before one can move to the experimental part of the research, they should first identify what they expect to see for results. At this stage, a scientist makes an educated guess and writes a hypothesis that he or she is going to prove or refute in the course of their study.
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A hypothesis can also be seen as a form of development of knowledge. It is a well-grounded assumption put forward to clarify the properties and causes of the phenomena being studied.
As a rule, a hypothesis is formed based on a number of observations and examples that confirm it. This way, it looks plausible as it is backed up with some known information. The hypothesis is subsequently proved by turning it into an established fact or refuted (for example, by pointing out a counterexample), which allows it to attribute it to the category of false statements.
As a student, you may be asked to create a hypothesis statement as a part of your academic papers. Hypothesis-based approaches are commonly used among scientific academic works, including but not limited to research papers, theses, and dissertations.
Note that in some disciplines, a hypothesis statement is called a thesis statement. However, its essence and purpose remain unchanged – this statement aims to make an assumption regarding the outcomes of the investigation that will either be proved or refuted.
Characteristics and Sources of a Hypothesis
Now, as you know what a hypothesis is in a nutshell, let’s look at the key characteristics that define it:
- It has to be clear and accurate in order to look reliable.
- It has to be specific.
- There should be scope for further investigation and experiments.
- A hypothesis should be explained in simple language—while retaining its significance.
- If you are making a relational hypothesis, two essential elements you have to include are variables and the relationship between them.
The main sources of a hypothesis are:
- Scientific theories.
- Observations from previous studies and current experiences.
- The resemblance among different phenomena.
- General patterns that affect people’s thinking process.
Types of Hypothesis
Basically, there are two major types of scientific hypothesis: alternative and null.
- Alternative Hypothesis
This type of hypothesis is generally denoted as H1. This statement is used to identify the expected outcome of your research. According to the alternative hypothesis definition, this type of hypothesis can be further divided into two subcategories:
- Directional — a statement that explains the direction of the expected outcomes. Sometimes this type of hypothesis is used to study the relationship between variables rather than comparing between the groups.
- Non-directional — unlike the directional alternative hypothesis, a non-directional one does not imply a specific direction of the expected outcomes.
Now, let’s see an alternative hypothesis example for each type:
Directional: Attending more lectures will result in improved test scores among students. Non-directional: Lecture attendance will influence test scores among students.
Notice how in the directional hypothesis we specified that the attendance of more lectures will boost student’s performance on tests, whereas in the non-directional hypothesis we only stated that there is a relationship between the two variables (i.e. lecture attendance and students’ test scores) but did not specify whether the performance will improve or decrease.
- Null Hypothesis
This type of hypothesis is generally denoted as H0. This statement is the complete opposite of what you expect or predict will happen throughout the course of your study—meaning it is the opposite of your alternative hypothesis. Simply put, a null hypothesis claims that there is no exact or actual correlation between the variables defined in the hypothesis.
To give you a better idea of how to write a null hypothesis, here is a clear example: Lecture attendance has no effect on student’s test scores.
Both of these types of hypotheses provide specific clarifications and restatements of the research problem. The main difference between these hypotheses and a research problem is that the latter is just a question that can’t be tested, whereas hypotheses can.
Based on the alternative and null hypothesis examples provided earlier, we can conclude that the importance and main purpose of these hypotheses are that they deliver a rough description of the subject matter. The main purpose of these statements is to give an investigator a specific guess that can be directly tested in a study. Simply put, a hypothesis outlines the framework, scope, and direction for the study. Although null and alternative hypotheses are the major types, there are also a few more to keep in mind:
Research Hypothesis — a statement that is used to test the correlation between two or more variables.
For example: Eating vitamin-rich foods affects human health.
Simple Hypothesis — a statement used to indicate the correlation between one independent and one dependent variable.
For example: Eating more vegetables leads to better immunity.
Complex Hypothesis — a statement used to indicate the correlation between two or more independent variables and two or more dependent variables.
For example: Eating more fruits and vegetables leads to better immunity, weight loss, and lower risk of diseases.
Associative and Causal Hypothesis — an associative hypothesis is a statement used to indicate the correlation between variables under the scenario when a change in one variable inevitably changes the other variable. A causal hypothesis is a statement that highlights the cause and effect relationship between variables.
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Hypothesis vs Prediction
When speaking of hypotheses, another term that comes to mind is prediction. These two terms are often used interchangeably, which can be rather confusing. Although both a hypothesis and prediction can generally be defined as “guesses” and can be easy to confuse, these terms are different. The main difference between a hypothesis and a prediction is that the first is predominantly used in science, while the latter is most often used outside of science.
Simply put, a hypothesis is an intelligent assumption. It is a guess made regarding the nature of the unknown (or less known) phenomena based on existing knowledge, studies, and/or series of experiments, and is otherwise grounded by valid facts. The main purpose of a hypothesis is to use available facts to create a logical relationship between variables in order to provide a more precise scientific explanation. Additionally, hypotheses are statements that can be tested with further experiments. It is an assumption you make regarding the flow and outcome(s) of your research study.
A prediction, on the contrary, is a guess that often lacks grounding. Although, in theory, a prediction can be scientific, in most cases it is rather fictional—i.e. a pure guess that is not based on current knowledge and/or facts. As a rule, predictions are linked to foretelling events that may or may not occur in the future. Often, a person who makes predictions has little or no actual knowledge of the subject matter he or she makes the assumption about.
Another big difference between these terms is in the methodology used to prove each of them. A prediction can only be proven once. You can determine whether it is right or wrong only upon the occurrence or non-occurrence of the predicted event. A hypothesis, on the other hand, offers scope for further testing and experiments. Additionally, a hypothesis can be proven in multiple stages. This basically means that a single hypothesis can be proven or refuted numerous times by different scientists who use different scientific tools and methods.
To give you a better idea of how a hypothesis is different from a prediction, let’s look at the following examples:
Hypothesis: If I eat more vegetables and fruits, then I will lose weight faster.
This is a hypothesis because it is based on generally available knowledge (i.e. fruits and vegetables include fewer calories compared to other foods) and past experiences (i.e. people who give preference to healthier foods like fruits and vegetables are losing weight easier). It is still a guess, but it is based on facts and can be tested with an experiment.
Prediction: The end of the world will occur in 2023.
This is a prediction because it foretells future events. However, this assumption is fictional as it doesn’t have any actual grounded evidence supported by facts.
Based on everything that was said earlier and our examples, we can highlight the following key takeaways:
- A hypothesis, unlike a prediction, is a more intelligent assumption based on facts.
- Hypotheses define existing variables and analyze the relationship(s) between them.
- Predictions are most often fictional and lack grounding.
- A prediction is most often used to foretell events in the future.
- A prediction can only be proven once – when the predicted event occurs or doesn’t occur.
- A hypothesis can remain a hypothesis even if one scientist has already proven or disproven it. Other scientists in the future can obtain a different result using other methods and tools.
We also recommend that you read about some informative essay topics .
Now, as you know what a hypothesis is, what types of it exist, and how it differs from a prediction, you are probably wondering how to state a hypothesis. In this section, we will guide you through the main stages of writing a good hypothesis and provide handy tips and examples to help you overcome this challenge:
1. Define Your Research Question
Here is one thing to keep in mind – regardless of the paper or project you are working on, the process should always start with asking the right research question. A perfect research question should be specific, clear, focused (meaning not too broad), and manageable.
Example: How does eating fruits and vegetables affect human health?
2. Conduct Your Basic Initial Research
As you already know, a hypothesis is an educated guess of the expected results and outcomes of an investigation. Thus, it is vital to collect some information before you can make this assumption.
At this stage, you should find an answer to your research question based on what has already been discovered. Search for facts, past studies, theories, etc. Based on the collected information, you should be able to make a logical and intelligent guess.
3. Formulate a Hypothesis
Based on the initial research, you should have a certain idea of what you may find throughout the course of your research. Use this knowledge to shape a clear and concise hypothesis.
Based on the type of project you are working on, and the type of hypothesis you are planning to use, you can restate your hypothesis in several different ways:
Non-directional: Eating fruits and vegetables will affect one’s human physical health. Directional: Eating fruits and vegetables will positively affect one’s human physical health. Null: Eating fruits and vegetables will have no effect on one’s human physical health.
4. Refine Your Hypothesis
Finally, the last stage of creating a good hypothesis is refining what you’ve got. During this step, you need to define whether your hypothesis:
- Has clear and relevant variables;
- Identifies the relationship between its variables;
- Is specific and testable;
- Suggests a predicted result of the investigation or experiment.
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Following a step-by-step guide and tips from our essay writers for hire , you should be able to create good hypotheses with ease. To give you a starting point, we have also compiled a list of different research questions with one hypothesis and one null hypothesis example for each:
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Sometimes, coping with a large academic load is just too much for a student to handle. Papers like research papers and dissertations can take too much time and effort to write, and, often, a hypothesis is a necessary starting point to get the task on track. Writing or editing a hypothesis is not as easy as it may seem. However, if you need help with forming it, the team at EssayPro is always ready to come to your rescue! If you’re feeling stuck, or don’t have enough time to cope with other tasks, don’t hesitate to send us you rewrite my essay for me or any other request.
Statistics Made Easy
How to Write a Null Hypothesis (5 Examples)
A hypothesis test uses sample data to determine whether or not some claim about a population parameter is true.
Whenever we perform a hypothesis test, we always write a null hypothesis and an alternative hypothesis, which take the following forms:
H 0 (Null Hypothesis): Population parameter =, ≤, ≥ some value
H A (Alternative Hypothesis): Population parameter <, >, ≠ some value
Note that the null hypothesis always contains the equal sign .
We interpret the hypotheses as follows:
Null hypothesis: The sample data provides no evidence to support some claim being made by an individual.
Alternative hypothesis: The sample data does provide sufficient evidence to support the claim being made by an individual.
For example, suppose it’s assumed that the average height of a certain species of plant is 20 inches tall. However, one botanist claims the true average height is greater than 20 inches.
To test this claim, she may go out and collect a random sample of plants. She can then use this sample data to perform a hypothesis test using the following two hypotheses:
H 0 : μ ≤ 20 (the true mean height of plants is equal to or even less than 20 inches)
H A : μ > 20 (the true mean height of plants is greater than 20 inches)
If the sample data gathered by the botanist shows that the mean height of this species of plants is significantly greater than 20 inches, she can reject the null hypothesis and conclude that the mean height is greater than 20 inches.
Read through the following examples to gain a better understanding of how to write a null hypothesis in different situations.
Example 1: Weight of Turtles
A biologist wants to test whether or not the true mean weight of a certain species of turtles is 300 pounds. To test this, he goes out and measures the weight of a random sample of 40 turtles.
Here is how to write the null and alternative hypotheses for this scenario:
H 0 : μ = 300 (the true mean weight is equal to 300 pounds)
H A : μ ≠ 300 (the true mean weight is not equal to 300 pounds)
Example 2: Height of Males
It’s assumed that the mean height of males in a certain city is 68 inches. However, an independent researcher believes the true mean height is greater than 68 inches. To test this, he goes out and collects the height of 50 males in the city.
H 0 : μ ≤ 68 (the true mean height is equal to or even less than 68 inches)
H A : μ > 68 (the true mean height is greater than 68 inches)
Example 3: Graduation Rates
A university states that 80% of all students graduate on time. However, an independent researcher believes that less than 80% of all students graduate on time. To test this, she collects data on the proportion of students who graduated on time last year at the university.
H 0 : p ≥ 0.80 (the true proportion of students who graduate on time is 80% or higher)
H A : μ < 0.80 (the true proportion of students who graduate on time is less than 80%)
Example 4: Burger Weights
A food researcher wants to test whether or not the true mean weight of a burger at a certain restaurant is 7 ounces. To test this, he goes out and measures the weight of a random sample of 20 burgers from this restaurant.
H 0 : μ = 7 (the true mean weight is equal to 7 ounces)
H A : μ ≠ 7 (the true mean weight is not equal to 7 ounces)
Example 5: Citizen Support
A politician claims that less than 30% of citizens in a certain town support a certain law. To test this, he goes out and surveys 200 citizens on whether or not they support the law.
H 0 : p ≥ .30 (the true proportion of citizens who support the law is greater than or equal to 30%)
H A : μ < 0.30 (the true proportion of citizens who support the law is less than 30%)
Introduction to Hypothesis Testing Introduction to Confidence Intervals An Explanation of P-Values and Statistical Significance
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Best Practices in Science
The Null Hypothesis
Journals and Blogs
The null hypothesis, as described by Anthony Greenwald in ‘Consequences of Prejudice Against the Null Hypothesis,’ is the hypothesis of no difference between treatment effects or of no association between variables. Unfortunately in academia, the ‘null’ is often associated with ‘insignificant,’ ‘no value,’ or ‘invalid.’ This association is due to the bias against papers that accept the null hypothesis by journals. This prejudice by journals to only accept papers that show ‘significant’ results (aka rejecting this ‘null hypothesis’) puts added pressure on those working in academia, especially with their relevance and salaries often depend on publications. This pressure may also be correlated with increased scientific misconduct, which you can also read more about on this website by clicking here . If you would like to read publication, journal articles, and blogs about the null hypothesis, views on rejecting and accepting the null, and journal bias against the null hypothesis, please see the resources we have linked below.
Most scientific journals are prejudiced against papers that demonstrate support for null hypotheses and are unlikely to publish such papers and articles. This phenomenon leads to selective publishing of papers and ensures that the portion of articles that do get published is unrepresentative of the total research in the field.
Journal of Articles in Support of the Null Hypothesis
Journal of Negative Results, Ecology and Evolutionary Biology
Journal of Negative Results in BioMedicine
Positively Negative: A New PLOS ONE Collection focusing on Negative, Null and Inconclusive Results
Journal of Interesting Negative Results in Natural Language Processing and Machine Learning
The All Results Journals
Journal of Contradicting Results in Science
Journal of Pharmaceutical Negative Results
Null Hypothesis Definition and Examples
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- Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
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In a scientific experiment, the null hypothesis is the proposition that there is no effect or no relationship between phenomena or populations. If the null hypothesis is true, any observed difference in phenomena or populations would be due to sampling error (random chance) or experimental error. The null hypothesis is useful because it can be tested and found to be false, which then implies that there is a relationship between the observed data. It may be easier to think of it as a nullifiable hypothesis or one that the researcher seeks to nullify. The null hypothesis is also known as the H 0, or no-difference hypothesis.
The alternate hypothesis, H A or H 1 , proposes that observations are influenced by a non-random factor. In an experiment, the alternate hypothesis suggests that the experimental or independent variable has an effect on the dependent variable .
How to State a Null Hypothesis
There are two ways to state a null hypothesis. One is to state it as a declarative sentence, and the other is to present it as a mathematical statement.
For example, say a researcher suspects that exercise is correlated to weight loss, assuming diet remains unchanged. The average length of time to achieve a certain amount of weight loss is six weeks when a person works out five times a week. The researcher wants to test whether weight loss takes longer to occur if the number of workouts is reduced to three times a week.
The first step to writing the null hypothesis is to find the (alternate) hypothesis. In a word problem like this, you're looking for what you expect to be the outcome of the experiment. In this case, the hypothesis is "I expect weight loss to take longer than six weeks."
This can be written mathematically as: H 1 : μ > 6
In this example, μ is the average.
Now, the null hypothesis is what you expect if this hypothesis does not happen. In this case, if weight loss isn't achieved in greater than six weeks, then it must occur at a time equal to or less than six weeks. This can be written mathematically as:
H 0 : μ ≤ 6
The other way to state the null hypothesis is to make no assumption about the outcome of the experiment. In this case, the null hypothesis is simply that the treatment or change will have no effect on the outcome of the experiment. For this example, it would be that reducing the number of workouts would not affect the time needed to achieve weight loss:
H 0 : μ = 6
- Null Hypothesis Examples
"Hyperactivity is unrelated to eating sugar " is an example of a null hypothesis. If the hypothesis is tested and found to be false, using statistics, then a connection between hyperactivity and sugar ingestion may be indicated. A significance test is the most common statistical test used to establish confidence in a null hypothesis.
Another example of a null hypothesis is "Plant growth rate is unaffected by the presence of cadmium in the soil ." A researcher could test the hypothesis by measuring the growth rate of plants grown in a medium lacking cadmium, compared with the growth rate of plants grown in mediums containing different amounts of cadmium. Disproving the null hypothesis would set the groundwork for further research into the effects of different concentrations of the element in soil.
Why Test a Null Hypothesis?
You may be wondering why you would want to test a hypothesis just to find it false. Why not just test an alternate hypothesis and find it true? The short answer is that it is part of the scientific method. In science, propositions are not explicitly "proven." Rather, science uses math to determine the probability that a statement is true or false. It turns out it's much easier to disprove a hypothesis than to positively prove one. Also, while the null hypothesis may be simply stated, there's a good chance the alternate hypothesis is incorrect.
For example, if your null hypothesis is that plant growth is unaffected by duration of sunlight, you could state the alternate hypothesis in several different ways. Some of these statements might be incorrect. You could say plants are harmed by more than 12 hours of sunlight or that plants need at least three hours of sunlight, etc. There are clear exceptions to those alternate hypotheses, so if you test the wrong plants, you could reach the wrong conclusion. The null hypothesis is a general statement that can be used to develop an alternate hypothesis, which may or may not be correct.
- What Are Examples of a Hypothesis?
- What Is a Hypothesis? (Science)
- What 'Fail to Reject' Means in a Hypothesis Test
- What Are the Elements of a Good Hypothesis?
- Scientific Hypothesis Examples
- Null Hypothesis and Alternative Hypothesis
- What Is a Control Group?
- Understanding Simple vs Controlled Experiments
- Six Steps of the Scientific Method
- Scientific Method Vocabulary Terms
- Definition of a Hypothesis
- Type I and Type II Errors in Statistics
- An Example of a Hypothesis Test
- How to Conduct a Hypothesis Test
- Hypothesis Test Example
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How to Write a Null Hypothesis
Home / Blog / How To Write A Null Hypothesis
When conducting the study, scientists and researchers create assumptions about their work and then attempt to confirm or refute those assumptions. These presumptions are also known as hypotheses; we will talk about the various kinds of hypotheses that are developed during the research paper help process. This article explains alternate and null hypotheses as well as the distinctions between them based on various parameters. Alternative hypotheses and the null hypothesis are two claims about the population that cannot both be true. On a sample of data, researchers run tests to decide whether to accept or reject the hypothesis.
Answering Your Research Question With Hypotheses
There are opposing explanations for your research question provided by the null and alternative hypotheses. "Does the independent variable affect the dependent variable?" is the research question's first part.
- According to the null hypothesis (H 0 ), the population is not affected.
- The alternative hypothesis (H a ) provides the affirmative response, "Yes, there is an effect in the population."
The alternative and null are both population-based assertions. The reason for this is that the purpose of hypothesis testing is to draw conclusions about a population from a sample. By examining disparities between groups or correlations between variables in the sample, we can frequently determine whether there is an effect on the population. A strong hypothesis must be written for your research.
To determine whether the evidence supports the null or alternative hypothesis, you might employ a statistical test. The null and alternative hypotheses must be stated in a precise form for each type of statistical test. The hypothesis can, however, also be stated in a more general manner that is applicable to any test.
What Is A Null Hypothesis?
The idea that there is no influence on the population is known as the null hypothesis.
We can reject the null hypothesis if the sample contains sufficient data to refute the assertion that there is no effect on the population (p ≤ a). If not, we are unable to rule out the null hypothesis.
Although the phrase "fail to reject" may sound uncomfortable, statisticians only accept it. Avoid using words like "prove" or "accept" when referring to the null hypothesis.
According to the null hypothesis, there is no correlation between the independent variable and the phenomenon being measured (the dependent variable). You can test the null hypothesis even if you don't think it's accurate. Instead, you will probably have a sneaking suspicion that a group of factors are related. Rejecting the null hypothesis is one technique to demonstrate that this is the case. A hypothesis being disproved does not imply that an experiment was "bad" or that no findings were obtained. In fact, it's frequently one of the first moves taken toward more research.
Examples of the null hypothesis
The average exam score at one school, according to the principal, is seven out of ten. The population mean of 7.0 is the null hypothesis. To test this null hypothesis, we collect the marks of, say, 30 students (a sample) from the school's total enrollment of, say, 300, and compute the sample mean.
Then, in an effort to reject the null hypothesis, we might contrast the (calculated) sample mean with the (hypothesized) population mean of 7.0. The sample data cannot be used to demonstrate the null hypothesis, which is that the population mean is 7.0. One option is to reject it.
Here null hypothesis is: The average exam grade for students at the school is seven out of ten.
One mutual fund in particular is said to have an 8% annual return. Consider a mutual fund that has been around for 20 years. The mutual fund's mean return is 8%, which is the null hypothesis. We get the sample mean by taking a random sample of the mutual fund's annual returns over, say, five years. The null hypothesis is then tested by comparing the sample mean (calculated) to the population mean (claimed), which is 8%.
Here null hypothesis is: The mutual fund's average yearly return is 8%.
What Is An Alternative Hypothesis?
The alternate response to your research question is the alternative hypothesis (Ha). It asserts that the population is affected. Your research hypothesis and your alternate hypothesis are frequently identical. It is, in other words, the assertion that you anticipate or hope will be accurate.
The complement of the null hypothesis is the alternative hypothesis. The extensive nature of null and alternative hypotheses ensures that they account for all potential outcomes. Additionally, they are mutually exclusive, thus only one of them may be true at once.
An assertion used in statistical inference experiments is known as the alternative hypothesis. It is indicated by Ha or H1 and runs counter to the null hypothesis. Another way to put it is that it is only a different option from the null.
An alternative theory in hypothesis testing is a claim that the researcher is testing. According to the researcher, this assertion is accurate and eventually supports rejecting the null hypothesis in favor of a different one. In this hypothesis, the researchers forecast the difference between two or more variables, ensuring that the test's observed data pattern is not the result of chance.
Examples of alternative hypotheses
The alternate theory in a clinical study for a new treatment can be that the new drug, on average, has a different effect than the present drug. We would write H1: The average effects of the two medications are different. Another possibility is that the new medication is generally superior to the old one. In this instance, we would write H1: The new drug is, on average, superior to the current drug.
The solubility of sugar increases with an increase in coffee's temperature.
Did you realize that coffee served hotter has more vitality than coffee served cold? Because of the additional energy, the water molecules in the coffee cup move more quickly, which in turn leads the sugar molecules to travel more quickly and dissolve. Because of this, the aforementioned alternate hypothesis is accurate.
Similarities And Differences Between Null And Alternative Hypotheses
Alternative and null hypotheses have some similarities:
1. Mutual exclusivity: Null and alternative hypotheses are mutually exclusive. If one is true, the other must be false. It means they cover all possible scenarios for the observed data. This ensures that the hypothesis test is comprehensive and there are no unaccounted data.
2. Statement of the research question: Both null and alternative hypotheses are statements about the population parameter or the effect being studied. They frame the research question and provide a clear statement of what the researcher is trying to investigate or test.
3. Subject to testing: Both hypotheses are subject to empirical testing using sample data. The goal of hypothesis testing is to determine the positive support. It means which of the hypotheses is more supported by the evidence.
4. Theoretical foundation: Null and alternative hypotheses are constructed based on theoretical or prior knowledge about the population being studied. The null hypothesis represents a default or status quo assumption. Whereas the alternative hypothesis represents a departure from that assumption.
5. Comparison: In hypothesis testing, the null hypothesis is typically a statement of no effect or no difference. E.g., no effect of a treatment, no difference between two groups. Whereas the alternative hypothesis is a statement of an effect or a difference. For, there is an effect of treatment; there is a difference between the two groups.
6. Decision-making: The process of hypothesis involves collecting data, calculating, and making a decision. It can define if the null hypothesis should be rejected or not. This decision is based on the evidence provided by the sample data.
7. Statistical tests: Both null and alternative hypotheses are used in conjunction with specific statistical tests that are chosen based on the research question and the nature of the data. The choice of test depends on whether the hypotheses involve means, proportions, variances, correlations, etc.
The following table summarizes the key differences between the two categories of hypotheses.
How To Write Null And Alternative Hypotheses?
Writing a null hypothesis (often denoted as H0) is a critical step in hypothesis testing. It serves as the default or status quo assumption that you aim to test against when analyzing your data. Below, I'll provide a template for custom writing a null hypothesis with an explanation for each section:
Template for Writing a Null Hypothesis (H0)
General Format: H0: [Population parameter or effect] is [equal to/less than/greater than] [specific value or condition].
1. H0 is where you state your null hypothesis. Begin with "H0:" to indicate that you are formulating the null hypothesis.
2. Population parameter or effect: Here, specify the population parameter or the effect you are studying. This should be a clear and concise description of what you are testing. For example, if you are conducting a study on the effect of a new drug on blood pressure, you might write "the mean blood pressure."
3. Equal to/less than/greater than Choose the appropriate relational operator based on the nature of your research question. You can use "equal to" if you want to test if the parameter or effect is equal to a specific value, "less than" if you want to test if it's less than a value, or "greater than" if you want to test if it's greater than a value.
4. Specific value or condition: In this part, specify the specific value or condition that represents the null hypothesis. This value should reflect the status quo or the assumption you want to test. For example, if you're testing the effect of a new drug on blood pressure and you believe it has no effect, you might use the average blood pressure of the control group as the specific value.
An alternative hypothesis (often denoted as "Ha" or "H1") is a critical reflection component of hypothesis testing in statistics and scientific research. It presents a statement that contrasts with the null hypothesis (H0) and represents the effect, difference, or relationship that researchers are interested in exploring. Here's how to write an alternative hypothesis effectively using a template in 300 words:
Template for Writing an Alternative Hypothesis:
Begin with a Clear Statement: Start by crafting a clear and concise statement that directly addresses the research question and the potential effect, difference, or relationship you are investigating.
Alternative Hypothesis Structure:
1. for two sample means:.
- "The [variable of interest] in [group 1] is significantly [greater/less than] the [variable of interest] in [group 2]."
- "There is a significant difference in [variable of interest] between [group 1] and [group 2]."
2. For Two Proportions:
- "The proportion of [event of interest] in [group 1] is significantly [greater/less than] the proportion of [event of interest] in [group 2]."
- "There is a significant difference in the proportions of [event of interest] between [group 1] and [group 2]."
3. For Regression Analysis:
- "The [independent variable(s)] has a significant [positive/negative] effect on the [dependent variable], as indicated by a [positive/negative] regression coefficient."
4. For Correlation Analysis:
- "There is a significant [positive/negative] correlation between [variable 1] and [variable 2]."
5. For Chi-Square Test of Independence:
- "There is a significant association between [variable 1] and [variable 2] in the population."
6. For ANOVA (Analysis of Variance):
- "At least one of the group means is significantly different from the others."
7. For Survival Analysis:
- "The survival curves of [group 1] and [group 2] are significantly different."
Frequently Asked Questions:
Q1. what is a null hypothesis (h0).
A null hypothesis is a statement that suggests there is no effect, difference, or relationship between variables in a research study. It serves as a starting point for hypothesis testing.
Q2. What is an alternative hypothesis?
An alternative hypothesis is a statement that contradicts the null hypothesis and represents the effect, difference, or relationship that researchers aim to investigate or detect.
Q3. Why is the null hypothesis important?
The null hypothesis provides a baseline for statistical testing, allowing researchers to assess whether observed data provide enough evidence to reject the default assumption and accept the alternative hypothesis.
Q4. Can you provide an example of a null hypothesis?
A classic example is in a drug trial: "There is no significant difference in blood pressure between the control group (receiving a placebo) and the treatment group (receiving the new drug)."
Q5. What is a one-tailed (directional) alternative hypothesis?
A one-tailed alternative hypothesis specifies a specific direction of the effect, such as "The new treatment reduces blood pressure significantly." It tests for an effect in one direction only.
Q6. What is a two-tailed (non-directional) alternative hypothesis?
A two-tailed alternative hypothesis tests for a difference or effect in either direction and does not specify a particular direction. For example, "There is a significant difference in blood pressure between the groups."
Q7. How do you choose between a one-tailed and a two-tailed alternative hypothesis?
The choice depends on your research question and whether you have a specific expectation about the direction of the effect. Use a one-tailed test when you have a directional hypothesis; use a two-tailed test when you're open to effects in either direction.
Q8. What is a Type I error?
A Type I error occurs when you incorrectly reject a true null hypothesis. It represents a false positive, indicating that you found an effect or difference when one does not actually exist.
Q9. What is a Type II error?
A Type II error happens when you fail to reject a false null hypothesis. It indicates that you did not detect an effect or difference that does exist in reality.
Q10. How do you determine the significance level (alpha) for hypothesis testing?
The significance level, often denoted as α, is predetermined by the researcher and represents the probability of making a Type I error. Common choices include α = 0.05 or α = 0.01, but it depends on the desired level of confidence in the test results.
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A Null and an Alternate Hypothesis Essay
Coming up with hypotheses from a number of research questions is quite an easy task. To achieve this, study the research questions keenly, and come up with a positive statement about the scenario; that is, a statement that suggests that there is a relationship between two variables, for research that seeks to establish the existence of a correlation between variables or come up with a statement that suggests a difference between groups. This will be an alternative hypothesis. On the other hand, a negative statement that does not suggest a relationship and difference respectively will be a null hypothesis (Siegle, n.d.).
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A null hypothesis, normally denoted by H o , is a proposition that forms the basis of an argument, by being proven to be either true or false. Normally, a null hypothesis is framed in such a way that it suggests the non-existence of a relationship between variables, or the non-existence of a difference between groups. The reason why the null hypothesis is structured this way is that the hypothesis is normally formulated for rejection. That is, as one formulates the null hypothesis, he/she intends to use it to determine the existence of a relationship between variables or investigate the similarity between two groups. A fact about the null hypothesis that can also e considered as its use is that the final findings of any statistical hypothesis test are presented in terms of the null hypothesis. Another thing to note is that it is considered unprofessional to accept the null hypothesis. In case, the analysis of research data shows that the null hypothesis is not false, one should say that he/she does not reject the null hypothesis (Easton, and McColl, 2006, p. 1). An example of a null hypothesis is:
- H o : there is no significant difference between drug A and drug B.
An alternative hypothesis, normally denoted by H 1 , is a proposition that is made to support what the statistical test intends to establish. That is, it is not formulated for rejection like the null hypothesis. Therefore, the alternative hypothesis is used to state the intention of the study, and test. It thus comes in handy, in situations where the null hypothesis is rejected because it gives the status quo of the variables or groups being tested (Easton, and McColl, 2006, p. 1). For instance, in the null hypothesis above, the intention of the study, and the test could have been to establish if a newly developed drug is better than an existent one. The alternative hypothesis for the given null hypothesis would therefore be something like:
- H 1 : drug A is better than drug B.
Therefore, for every null hypothesis, there should be an alternative hypothesis that is framed in such a way that it is more or less the opposite of the null hypothesis.
As evidenced in the discussion above, the alternative hypothesis and the null hypothesis are literally competing. The hypotheses are thus “mutually exclusive and exhaustive” (Easton, and McColl, 2006, p. 1). It is also evident that the null hypothesis is always assumed to e true, although it is formulated for rejection. It is thus true that the null hypothesis is structured in such a way that it will prove that a certain situation is not like it may be thought to be, in case it is rejected, or that a certain situation is as it is thought to be. In the latter case, it is not rejected.
Easton, V. J., & McColl, J., H. (2006). Hypothesis Testing .
Siegle, D. (n.d.). Null and Alternative Hypotheses. Web.
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Null Hypothesis Generator with Examples
Fill in the fields to get a hypothesis
Add above the person or phenomenon you are focusing on in your study.
Add above the activity or characteristic of your research group). Start with a verb correlating with your subject.
Add above the thing that you are going to measure in your study.
Add above the person or phenomenon you are comparing with your research group.
Add here the effect of the predicate on the dependent variable.
Are you looking for a free null hypothesis generator? Writing a hypothesis is important in academic writing since it shows the direction of your research paper. Don’t stress yourself with endless research hours; try our efficient hypothesis generator and get instant results. It is free, simple, and accessible online for students at any academic level. You can formulate a correct hypothesis within minutes, regardless of your paper’s complexity.
- 🤖 How to Use the Tool
- 🧩 What Is a Null Hypothesis
❌ How to Reject a Null Hypothesis
- ❗ References
🤖 Null Hypothesis Generator: How to Use It
A null hypothesis generator is simple to use. You no longer have to write wrong hypotheses because this generator provides accurate results for your research project.
You have to add correct information about your research paper in the provided fields below:
- The subject of your research or experimental group – who or what.
- What the subject or group does – independent variable .
- The measured thing – dependent variable .
- The result.
- The control group (optional).
After filling out the above fields, you can click on the button, and the system will generate results. Ensure you provide accurate information to get relevant results that align with your research question .
🧩 What Is a Null Hypothesis & Why Is It Important?
A null hypothesis is a statement that claims there is no relationship between a dependent and independent variable . The hypothesis is not supposed to show any connection; if it does, the research could have a sampling or experimental error. Thus, a false null hypothesis shows a relationship in the measured observation.
The null hypothesis is vital in research since it determines whether two measured observation subjects are related. It also lets the user know if the outcome is a product of chance or manipulation. However, the hypothesis must be tested to evaluate if it is true or false. The test determines if the null hypothesis should be accepted or rejected.
Researchers often use 2 strategies to test a null hypothesis:
- Significance testing;
- Hypothesis testing.
Both approaches are distinguished based on the observed data. Thus, a null hypothesis is important in research since it will reveal the direction of your essay.
👀 Null Hypothesis Examples
To formulate a null hypothesis, you must drive your statement from a question. Rewrite that question in a different format that presumes there is no relationship between the subjects of observation.
Here are examples of a null hypothesis.
When formulating a null hypothesis, you should always assume that the variables have no relationship. Always start with a question if you want to create a good null hypothesis that reflects your research.
When testing the null hypothesis, the p-value is important in determining the outcome of the observed variables. The p-value in statistics is a number that is calculated from a test. It illustrates the probability of getting the results if the null hypothesis is true.
In short, this value helps researchers decide whether the null hypothesis will be rejected .
Understanding the aspects that lead to the rejection of null hypothesis is essential. The entire process is vital in data analysis , interpretation, and calculations. Besides, you will be able to improve your analytical and research skills in different industries.
Therefore, rejecting a null hypothesis is possible if you follow the falsifiability principle .
What does it mean?
It means that a hypothesis may be regarded as scientifically valid only if it can be tested and proved or disproved based on the resulting data. Therefore, both null and alternative hypotheses should be falsifiable to inform a scholarly inquiry.
So, you must first establish the null hypothesis before testing.
Follow the 4 steps below:
- Establish the hypothesis;
- Create an experimental outline to test the null hypothesis;
- Perform the experiments;
- Interpret the outcome of the investigation.
There are some factors involved when it comes to rejecting a null hypothesis. The p-value is used as an indicator to reject the hypothesis if it is less or equal to the significance level. This level reveals the difference between the test outcome and the null hypothesis.
Therefore, you can use our null hypothesis maker to simplify your work and generate accurate results. Whether you are working on a biology research paper or a statistics paper, understanding how to identify and reject a null hypothesis is important.
We hope the tool and the information were helpful. You are welcome to try our other online writing apps to quickly polish your psychology essay.
❓ Null Hypothesis Generator FAQ
❓ what is a null hypothesis.
A null hypothesis is a statement that claims there is no relationship between independent and independent variables. It is usually based on insufficient evidence that needs more testing to prove if the observed information is true or false.
❓ How to write a null hypothesis?
Writing a null hypothesis requires you to ask a question. You need to rephrase the question in a form that makes no assumptions about the connection between the variables. Formulate the null hypotheses to show the treatment is ineffective.
❓ What does it mean to reject the null hypothesis?
A null hypothesis states that no difference exists between a set of groups. It means that the dependent variable doesn’t have a substantial impact effect on the independent variable. When researchers reject a null hypothesis, they have proven the alternative hypothesis. So, the final result shows there is a relationship between the variables.
❓ What is a null hypothesis example?
An example of a null hypothesis can start with this question: Are adults better at music than children? The null hypothesis, in this case, will state that age doesn’t affect musical ability. Thus, there is no relationship between music and a person’s age.
- Hypothesis Definition & Examples - Simply Psychology
- Hypothesis - APA Dictionary of Psychology
- Why Are Statistics Necessary in Psychology? - Verywell Mind
- Scientific Inquiry Definition: How the Scientific Method Works
- Karl Popper Life & Theory of Falsification - Study.com
Free Essay Hypothesis Generator
Writing an essay hypothesis might be challenging: it has to be well-structured and follow specific standards. But we have a solution! Our free essay hypothesis generator is here to help you. Check it out and take your writing to the next level!
- If high school students receive sex education, then their ability to prevent unwanted pregnancy will improve as compared to those who do not receive sex education.
- If high school students receive sex education, then their ability to prevent unwanted pregnancy will show better results than those who do not receive sex education.
- H0 (null hypothesis) - If high school students receive sex education, it has no effect on their ability to prevent unwanted pregnancy as compared to those who do not receive sex education.
- H1 (alternative hypothesis) - If high school students receive sex education, it has a negative effect on their ability to prevent unwanted pregnancy as compared to those who do not receive sex education.
- If high school students receive sex education, then their ability to prevent unwanted pregnancy will worsen as compared to those who do not receive sex education.
- If high school students receive sex education, then their ability to prevent unwanted pregnancy will show worse results than those who do not receive sex education.
- H1 (alternative hypothesis) - If high school students receive sex education, it has a positive effect on their ability to prevent unwanted pregnancy as compared to those who do not receive sex education.
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- 🤔 What Is an Essay Hypothesis?
- ✍️ How to Write a Hypothesis?
- ✨ Hypothesis Examples
👌 Hypothesis Maker: the Benefits
🔗 references, ✅ why use our essay hypothesis generator.
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🤔 Null and Alternative Hypothesis
An essay hypothesis is a claim that predicts the outcome of your research and tests the relationship between two variabilities . Your research can either prove or disprove it.
Like a thesis statement, an essay hypothesis is an answer to your research question. However, there is a difference:
- A hypothesis shows your ability to make a logical assumption. It can be supported by observations, statistical analysis, experiments, or other scientific research methods. It's usually used for quantitative analysis .
- A thesis statement defines your research and sums up its main points. We can use it for both quantitative and qualitative analysis. A thesis can be formulated manually or generated automatically .
Components of a Hypothesis
To write a solid hypothesis, you should define your variables first. There are two types of them: independent and dependent :
- An independent variable can be controlled by a researcher during an experiment.
- A dependent variable can only be measured and observed.
Let’s take a closer look.
Suppose your research is about the effect of having pets on mental well-being. Your hypothesis might be:
Spending more time playing with your pet positively affects one's mental health.
Here, the amount of time spent playing with a pet is an independent variable because this is something we can change and control. Therefore, the positive effect is the dependable variable since we can only observe it.
Types of Hypotheses
A hypothesis may vary depending on the goal and nature of your research. So, let’s break down several types of them.
- A statistical hypothesis tests a limited sample of a population . It uses statistics to calculate the result.
- A simple hypothesis tests a relationship between two variables and predicts the potential result of the research.
- An empirical or working hypothesis is based on factual data. It is used to create a theory for testing .
- A complex hypothesis tests a relationship between two or more dependent or independent variables.
- A logical hypothesis predicts the result of your research with no evidence or actual data but based on deduction.
- A null hypothesis is created to show that there is no relationship between variables. It is abbreviated as H0.
- An alternative hypothesis disproves a null hypothesis and states the opposite. It is abbreviated as H1 or HA.
✍️ How to Make a Hypothesis for an Essay
So, now you know what a hypothesis is. But how exactly do you write it? Here’s a step-by-step process.
Knowing the hypothesis will help you create an outline and topic sentences for your paper.
✨ Good Essay Hypothesis Examples
To understand what an essay hypothesis should be like, check out the examples below and see what makes them good.
We hope this article was helpful for you. Check out our essay hypothesis generator, topic generator , sentence rephraser , and other tools. Good luck with your studies!
❓ Hypothesis Maker FAQ
📍 how do you write a hypothesis for an essay, 📍 what is an example of a hypothesis, 📍 what are the 3 types of hypotheses.
- A simple hypothesis introduces a relationship between an independent and dependent variable.
- A complex one includes the relationship between two or more variables.
- Finally, a directional hypothesis includes the direction of the expected result.
📍 How to introduce a hypothesis in an essay?
- How to Write a Hypothesis: Grammarly
- Research Hypothesis: Oakland University
- How to Write a Hypothesis to an Analytical Essay: Classroom
- What Is the Difference Between a Thesis Statement and a Hypothesis Statement?: American Public University System
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Null Hypothesis and Research Hypothesis, Essay Example
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The difference between Null Hypothesis and Research Hypothesis lies in the characteristics of the population. When using Null Null Hypothesis, the researcher assumes that the population means are equal. Research Hypotheses are used when the population means are not equal.
The Null Hypothesis is used to determine whether or not the Research Hypothesis is correct. If the Null Hypothesis is rejected, it confirms the validity of the Research Hypothesis. By accepting that the Research Hypothesis is correct, researchers can identify a relationship between the independent variable and the dependent variable.
The main difference between the Null Hypothesis and the Research Hypothesis is their purpose. Null Hypotheses are applied to phenomenon that the researcher tries to disprove (nullify). The Null Hypothesis needs to be specific to the research question, for example: “Socioeconomic status does not affect health outcomes”. As a contrast, the Research Hypothesis is what the researcher is trying to prove, related to the question. As an example, a research hypothesis could be “Health outcomes vary based on geographical location in the United States, and are usually better in suburban areas than in urban communities”. If the Null Hypothesis is not confirmed and rejected, the impact of socio-economic status is found to have no significance on the research results.
The rejection of the Null Hypothesis means that there is only a little probability for a random error during the research. This stated, using the Null Hypothesis serves the purpose of proving the statistical significance of the research. To reject a Null Hypothesis, researchers usually apply a significance level of .05 or .01. This indicates that the Null Hypothesis is not confirmed, therefore, the Research Hypothesis can be used for inferential statistics.
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Type of paper: Essay
Topic: Family , Parents , Children , Students , Activism , Parental , Involvement , Theory
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Writing Different Types of Hypothesis
Writing Different Types of Hypothesis Parents who assume that the intelligence of children and their achievement ability is mainly because they are lucky enough to obtain higher abilities, they often do not see the point of involving themselves in the education of their children. They assume that children’s innate ability will have particular limitation about their achievement so that things that involve encouraging children or participating in school meetings are but a waste of time. On the other hand, parents who believe that performance of children in school is due to efforts, and their abilities and those child skills usually can be developed have the slightest chance to have positive reactions to parental involvement. Parents who understand that the way they raise their children will have a significant influence on their growth are much more likely to be positive about parental involvement than parents who believe that they can have little impact on their children’s development. A possible obstacle to the involvement of parents is their insight of the explicit level and contained invitations for participation (Hanafi & Lynch, 2002). When parents believe that teacher or school does not value parental involvement, they are not likely involved as much.
Schools that are friendly to parents, and make it obvious that parent’s participation is necessary, develop practices that are more effective than schools that do not appear inviting to parents.
Non-directional Hypothesis: Parents often consider schools as large routine organizations that are not friendly to parents, which are deemed one of the purposes for their trend for higher levels of parental participation.
Hanafin, J., & Lynch, A. (2002). Peripheral voices: Parental involvement, social class, and educational disadvantage. British Journal of Sociology of Education, 23(1), 35-49.
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