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.).
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 And Alternative Hypothesis
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The null hypothesis and alternative hypothesis are cornerstones in statistics and hypothesis testing. They represent a methodology used to validate or refute claims about a population based on sample data. The process involves assuming the null hypothesis is true and using statistical analysis to test if the observed data fit this assumption. This article delves into the key roles, calculations, and formulation of the null and alternative hypotheses.
- 1 Null and Alternative Hypothesis – In a Nutshell
- 2 Definition: Null and alternative hypotheses
- 3 Null/Alternative hypotheses: Research questions
- 4 The null hypothesis (H0)
- 5 The alternative hypothesis (Ha)
- 6 Null hypothesis vs. alternative hypothesis
- 7 Writing null and alternative hypotheses correctly
Null and Alternative Hypothesis – In a Nutshell
This article describes the academic conventions you need to follow when writing about these hypotheses, including:
- The specific wording to be used.
- Which tests can be used to test the hypotheses.
- Similarities and differences between both hypotheses.
- Statistical expressions you can use to write your conclusions regarding each hypothesis.
Definition: Null and alternative hypotheses
These two hypotheses are used in statistical testing to prove or disprove a theory.
- The null hypothesis always states that there’s no statistically significant relationship between variables.
- The alternative hypothesis states the opposite.
The null hypothesis (H 0 ) assumes there is no significant difference between specified populations or no association among groups. This is often considered the default or status quo hypothesis, indicating no change or effect.
The alternative hypothesis (H a or H 1 ) is the counterpart to the null hypothesis and claims that there is a significant difference or association among groups. It represents a statement of what a statistical hypothesis test is set up to establish.
You want to test whether a drug has an effect on a disease.
- Null hypothesis: There is no effect of the drug on the disease
- Alternative hypothesis: There is an effect (either positive or negative) of the drug on the disease.
Null/Alternative hypotheses: Research questions
These hypotheses function as tentative answers to research questions . Therefore, you can’t have an answer to your research questions without confirming or rejecting either hypothesis.
Both hypotheses are tested using statistical tests that compare two population samples/groups. Testing confirms or rejects the hypotheses, by showing whether there’s a relationship between an independent variable and a dependent variable.
The null hypothesis (H 0 )
The null hypothesis states that there’s no statistically significant relationship or effect between variables.
Based on test results, the null hypothesis can be rejected, which means there is a significant relationship between the variables – one affects the other.
Null hypothesis (H 0 ): The number of hours of sleep has no effect on short-term memory.
- If statistical testing shows that hours of sleep do affect memory, H 0 is rejected .
- If testing shows that memory stays the same irrespective of hours of sleep, you fail to reject H 0 .
When writing about null hypotheses, you can only reject them or fail to reject them. Don’t use expressions like accepting, proving, or disproving.
Depending on sample size and testing method, you can incur errors when determining the validity of null hypotheses.
- A Type I error happens if you reject H 0 and claim there’s a significant relationship between the variables, even though test results don’t support this claim.
- A Type II error happens if you fail to reject H 0 and claim there’s no relationship between variables, even though testing proves otherwise.
Examples of null hypotheses
The table below illustrates null hypotheses for their respective research question:
Common statistical tests used to reject H 0 include:
- Pearson correlation
- Linear regression
In the paper’s Methods section, you must indicate which test you used.
The alternative hypothesis (H a )
The alternative hypothesis ( H a or H 1 ) claims there’s a statistically significant relationship between variables.
Because H a is the opposite of what the null hypothesis claims, accepting H a means rejecting H 0 and vice versa.
When reporting alternative hypotheses, you can only say that H a is supported or not supported by test data. Don’t use expressions like accept, reject, disprove, confirm, etc.
Examples of alternative hypotheses
The table below shows alternative hypotheses for their respective research question:
Common statistical tests used to support the alternative hypothesis include:
Null hypothesis vs. alternative hypothesis
Both hypotheses provide possible but mutually exclusive answers to a research question. They can only be rejected or supported through statistical testing.
The differences between them are:
Writing null and alternative hypotheses correctly
To write these hypotheses correctly in your essay, make sure you:
- Write your research question, mentioning both independent and dependent variables
- State the null hypothesis
- State the alternative hypothesis.
Does exercise improve depressive symptoms?
Exercise = independent variable
Depressive symptoms = dependent variable
- H 0 = Exercise doesn’t improve depressive symptoms.
- H a = Exercise improves depressive symptoms.
For specific tests, use the following wording:
What are null and alternative hypotheses?
They’re unproven statements about a research question.
The null hypothesis says there’s a relationship between variables, and the alternative hypothesis claims there isn’t one.
Can I only test one hypothesis?
No, these hypotheses are competing statements, so when you test one hypothesis, you automatically test the other.
How do I write H0 and Ha using mathematical symbols?
The mathematical symbol used to write H 0 is = For H a , the symbol is ≠
Which type of tests can I use to test H0 and Ha?
In most cases, one-tailed tests are best.
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- Null and Alternative Hypotheses | Definitions & Examples
Null & Alternative Hypotheses | Definitions, Templates & Examples
Published on May 6, 2022 by Shaun Turney . Revised on June 22, 2023.
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 or H 1 ) : There’s an effect in the population.
Table of contents
Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, similarities and differences between null and alternative hypotheses, how to write null and alternative hypotheses, other interesting articles, frequently asked questions.
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.”
- 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. It’s critical for your research to write strong hypotheses .
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.
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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 ≤).
You can never know with complete certainty whether there is an effect in the population. Some percentage of the time, your inference about the population will be incorrect. When you incorrectly reject the null hypothesis, it’s called a type I error . When you incorrectly fail to reject it, it’s a type II error.
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.
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.
Test-specific template sentences
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.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
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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.
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.
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 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.
Applying Null and Alternative Hypotheses
The null hypothesis is a hypothesis which states that there is “no effect, no difference, or no relationship among the variables being studied” (Smith, Gratz, & Bousquet, 2009, p. 161). Conversely, the alternative hypothesis is a hypothesis that claims that there is an effect, a relationship, or a difference between the studied variables. These two hypotheses are opposites (Smith et al., 2009).
The reason for employing both types of hypotheses is the precision of the study. For instance, the null hypothesis, which denies the relationship between the variables, is precise (and, as a precise universal statement, it is relatively simple to reject: it’s only needed to find a significant counterexample), whereas the alternative hypothesis, according to which there is some type of relationship, is imprecise.
Therefore, in studies, the alternative hypothesis is not proved or disproved; on the contrary, researchers either fail to reject the null hypothesis (and find no support for the alternative hypothesis) or reject the null hypothesis (and find evidence that supports the alternative hypothesis) (Gravetter & Wallnau, 2008; Smith et al., 2009).
Simon-Dack (2014) measured the difference in students’ performance on four tests taken before and after participating in a learning activity aimed at exploring the action potential. The four null hypotheses for the study state that there would be no difference in the students’ performance before and after the activity on the four tests. Each of the four alternative hypotheses states that there would be a difference on one of the four tests the students took.
The conclusions from this study are as follows: there is a significant difference between the performance on three of the four tests taken before and after the activity; these tests are multiple-choice questions, the assessment of one’s belief in one’s understanding of the subject, and the essay questions; p values are.002,.031, and.024, respectively. Therefore, these three null hypotheses were rejected, and the three alternative hypotheses were supported. However, there was no significant difference in the scores of the tests measuring the students’ belief about how well they can explain the materials; p =.103. Thus, the corresponding null hypothesis was not rejected.
Gravetter, F., & Wallnau, L. (2008). Essentials of statistics for the behavioral sciences (6th ed.). Belmont, CA: Thomson Wardsworth.
Simon-Dack, S. L. (2014). Introducing the action potential to psychology students. Teaching of Psychology, 41 (1), 73-77. Web.
Smith, L. F., Gratz, Z. S., & Bousquet, S. G. (2009). The art and practice of statistics . Belmont, CA: Cengage Learning.
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Once you have developed a clear and focused research question or set of research questions, you’ll be ready to conduct further research, a literature review, on the topic to help you make an educated guess about the answer to your question(s). This educated guess is called a hypothesis.
In research, there are two types of hypotheses: null and alternative. They work as a complementary pair, each stating that the other is wrong.
- Null Hypothesis (H 0 ) – This can be thought of as the implied hypothesis. “Null” meaning “nothing.” This hypothesis states that there is no difference between groups or no relationship between variables. The null hypothesis is a presumption of status quo or no change.
- Alternative Hypothesis (H a ) – This is also known as the claim. This hypothesis should state what you expect the data to show, based on your research on the topic. This is your answer to your research question.
Null Hypothesis: H 0 : There is no difference in the salary of factory workers based on gender. Alternative Hypothesis : H a : Male factory workers have a higher salary than female factory workers.
Null Hypothesis : H 0 : There is no relationship between height and shoe size. Alternative Hypothesis : H a : There is a positive relationship between height and shoe size.
Null Hypothesis : H 0 : Experience on the job has no impact on the quality of a brick mason’s work. Alternative Hypothesis : H a : The quality of a brick mason’s work is influenced by on-the-job experience.
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How to Write a Null and Alternative Hypothesis: A Guide with Examples
8 December 2023
When undertaking a qualitative or quantitative research project, researchers must first formulate a research question, from which they develop a hypothesis. By definition, a hypothesis is a prediction that a researcher makes about the research question and can either be affirmative or negative. In this case, a research question has three main components: variables (independent and dependent), a population sample, and the relation between the variables. When the prediction contradicts the research question, it is referred to as a null hypothesis. In short, a null hypothesis is a statement that implies there is no relationship between independent and dependent variables. Hence, researchers need to learn how to write a good null and alternative hypothesis to present quality studies.
General Aspect of Writing a Null and Alternative Hypothesis
Students with qualitative or quantitative research assignments must learn how to formulate and write a good research question and hypothesis. By definition, a hypothesis is an assumption or prediction that a researcher makes before undertaking an experimental investigation. Basically, academic standards require such a prediction to be a precise and testable statement, meaning that researchers must prove or disapprove of it in the course of the assignment. In this case, the main components of a hypothesis are variables (independent and dependent), a population sample, and the relation between the variables. Therefore, a research hypothesis is a prediction that researchers write about the relationship between two or more variables. In turn, the research inquiry is the process that seeks to answer the research question and, in the process, test the hypothesis by confirming or disapproving it.
Types of Hypotheses
There are several types of hypotheses, including an alternative hypothesis, a null hypothesis, a directional hypothesis, and a non-directional hypothesis. Basically, the directional hypothesis is a prediction of how the independent variable affects the dependent variable. In contrast, the non-directional hypothesis predicts that the independent variable influences the dependent variable, but does not specify how. Regardless of the type, all hypotheses are about predicting the relationship between the independent and dependent variables.
What Is a Null and Alternative Hypothesis
A null hypothesis, usually symbolized as “H0,” is a statement that contradicts the research hypothesis. In other words, it is a negative statement, indicating that there is no relationship between the independent and dependent variables. By testing the null hypothesis, a researcher can determine whether the inquiry results are due to the chance or the effect of manipulating the dependent variable. In most instances, a null hypothesis corresponds with an alternative hypothesis, a positive statement that covers a relationship that exists between the independent and dependent variables. Also, it is highly recommendable that a researcher should write the alternative hypothesis first before the null hypothesis.
10 Examples of Research Questions with H0 and H1 Hypotheses
Before developing a hypothesis, a researcher must formulate the research question. Then, the next step is to transform the question into a negative statement that claims the lack of a relationship between the independent and dependent variables. Alternatively, researchers can change the question into a positive statement that includes a relationship that exists between the variables. In turn, this latter statement becomes the alternative hypothesis and is symbolized as H1. Hence, some of the examples of research questions and hull and alternative hypotheses are as follows:
1. Do physical exercises help individuals to age gracefully?
A Null Hypothesis (H0): Physical exercises are not a guarantee for graceful old age.
An Alternative Hypothesis (H1): Engaging in physical exercises enables individuals to remain healthy and active into old age.
2. What are the implications of therapeutic interventions in the fight against substance abuse?
H0: Therapeutic interventions are of no help in the fight against substance abuse.
H1: Exposing individuals with substance abuse disorders to therapeutic interventions help control and even stop their addictions.
3. How do sexual orientation and gender identity affect the experiences of late adolescents in foster care?
H0: Sexual orientation and gender identity have no effects on the experiences of late adolescents in foster care.
H1: The reality of stereotypes in society makes sexual orientation and gender identity factors complicate the experiences of late adolescents in foster care.
4. Does income inequality contribute to crime in high-density urban areas?
H0: There is no correlation between income inequality and incidences of crime in high-density urban areas.
H1: The high crime rates in high-density urban areas are due to the incidence of income inequality in those areas.
5. Does placement in foster care impact individuals’ mental health?
H0: There is no correlation between being in foster care and having mental health problems.
H1: Individuals placed in foster care experience anxiety and depression at one point in their life.
6. Do assistive devices and technologies lessen the mobility challenges of older adults with a stroke?
H0: Assistive devices and technologies do not provide any assistance to the mobility of older adults diagnosed with a stroke.
H1: Assistive devices and technologies enhance the mobility of older adults diagnosed with a stroke.
7. Does race identity undermine classroom participation?
H0: There is no correlation between racial identity and the ability to participate in classroom learning.
H1: Students from racial minorities are not as active as white students in classroom participation.
8. Do high school grades determine future success?
H0: There is no correlation between how one performs in high school and their success level in life.
H1: Attaining high grades in high school positions one for greater success in the future personal and professional lives.
9. Does critical thinking predict academic achievement?
H0: There is no correlation between critical thinking and academic achievement.
H1: Being a critical thinker is a pathway to academic success.
10. What benefits does group therapy provide to victims of domestic violence?
H0: Group therapy does not help victims of domestic violence because individuals prefer to hide rather than expose their shame.
H1: Group therapy provides domestic violence victims with a platform to share their hurt and connect with others with similar experiences.
Summing Up on How to Write a Null and Alternative Hypothesis
The formulation of research questions in qualitative and quantitative assignments helps students develop a hypothesis for their experiment. In this case, learning how to write a good hypothesis that helps students and researchers to make their research relevant. Basically, the difference between a null and alternative hypothesis is that the former contradicts the research question, while the latter affirms it. In short, a null hypothesis is a negative statement relative to the research question, and an alternative hypothesis is a positive statement. Moreover, it is important to note that developing the null hypothesis at the beginning of the assignment is for prediction purposes. As such, the research work answers the research question and confirms or disapproves of the hypothesis. Hence, some of the tips that students and researchers need to know when developing a null hypothesis include:
- Formulate a research question that specifies the relationship between an independent variable and a dependent variable.
- Develop an alternative hypothesis that says a relationship that exists between the variables.
- Develop a null hypothesis that says a relationship that does not exist between the variables.
- Conduct the research to answer the research question, which allows the confirmation of a disapproval of a null hypothesis.
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