Hypothesis testing is mainly used to eliminate sampling error (chance) as a possible explanation for any fallouts obtained from a research study. As you will see in this article, hypothesis testing is a method often relied upon to help student researchers establish whether a given treatment contains an effect on the subjects of a population. Apart from getting its official definition and a suitable example, you will also get to learn about the steps followed in hypothesis testing.
Hypothesis Testing – FAQs
Before it comes to printing & binding your dissertation you need to do your research, which often includes hypothesis tests. There is no variation in the manner in which hypothesis tests are performed or written. In hypothesis testing, the researcher should start by stating the hypothesis that they intend to examine. From here, they will need to formulate a plan on how to conduct the analysis, and then study their sample data. Sample data analysis is then followed by the acceptance or rejection of the null hypothesis established earlier.
An examinable hypothesis is never a simple statement. The researcher needs to come up with an intricate statement capable of providing a flawless overview of the scientific experiment at hand. It should also state the intentions of the experiment and the outcomes likely to be achieved. During hypothesis testing, you will need to consider the following:
I. Start by stating the problem you would like to solve
II. If possible, ensure the hypothesis you craft appears in the form of an if-then statement
III. Outline all your variables
The simple hypothesis refers to the prediction of the association that exists amongst two variables, namely the dependent variable and the independent one.
A good hypothesis ought to comprise of three main distinct sections: problem definition, proposed solutions, and the outcome (result).
Scientists often use it to study specific predictions known as “hypotheses,” which they formulate from theories.
The hypothesis testing process mainly involves five steps:
I. State the research hypothesis by specifying whether it is an alternate (Ha) hypothesis or a null (H0) hypothesis.
II. Gather data in a manner that is designed to help you examine the hypothesis
III. Conduct a suitable statistical test
IV. Elect whether your null hypothesis is refuted or supported
V. Put forward what you have found in your discussion or results section
Hypothesis Testing: Definition
In statistics, hypothesis testing is considered an act where an analyst or researcher attempts to study a statement related to a given population parameter. The reason for this analysis and the data currently available determines the research methodology that the analyst will apply.
Hypothesis Testing: Step by Step Guide
As mentioned elsewhere in this article, hypothesis testing is a process used to examine the concepts that a researcher has about the world and the population around them.
Hypothesis Testing 1st Step: Start by Stating the Hypotheses
You will need to state the hypotheses according to the following order:
I. Research Hypothesis
II. Null Hypothesis
III. And last but not least the Alternate Hypothesis
As you state the hypotheses, there will also be a need for you to recall what differentiates a general hypothesis that can’t be reversed following a single investigation and the alternate and null hypothesis.
Hypothesis Testing 2nd Step: Assumptions
For hypothesis testing, you will need to include:
i. Data level measurements
ii. Distributions that underlie your information
iii. Available data or the lack of information related to population features
iv. Sample methods and size
v. Sample features needed to apply your measurement statistics
vi. testing significance
Hypothesis Testing 3rd Step: Confidence Interval Structure or Test Statistic
Here you are required to specify:
- The structure you will need to use for you to test the set of confidence intervals or test significance levels (ensure you also include the notation and the required equations).
- All the special conditions that the statistic will need to meet.
Hypothesis Testing 4th Step: Probability Statement or Rejection Region
A rejection region refers to the expected measure of your examination statistic as made by the critical valve or tables for a confidence interval. Before you can start performing your calculations, it will be necessary for you to let the reader know how that particular test will be applied to discard the null hypothesis. Also, ensure you communicate the critical value you intend to use to make your determination.
Hypothesis Testing 5th Step: Annotated Spreadsheet (Calculations)
An annotated spreadsheet refers to the actual confidence interval or test statistic measure that you will generate. It should include a detailed specification of any extra equations applied as well as their notation. In some cases, you can also incorporate the sample calculations.
The process of solving a problem can be viewed as an art or as a skill. In terms of skills, you will need to break your problem down into tiny modules or parts. Once broken down, you will need to ensure you keep on checking on them using a hand calculator, sample calculations, or any other methods you may have in mind. It helps to ascertain that your solution doesn’t have any errors.
As mentioned earlier, problem-solving can also be viewed as art. Depending on your skill level, you will find that there are numerous ways of laying out a given task. Applying some elegance to this process guarantees that even the uninformed reader will understand what it is that you are trying to do. Furthermore, a problem that has been broken down into constituent sections always appears simple.
Hypothesis Testing 6th Step: Conclusions
The final step is all about making a statement of your results. It’s also known as the acceptance or discarding of your null hypothesis. In this last step, a researcher also gets to provide a direction for any research they intend to conduct in the future.
When making conclusions, it’s important to provide a summary of your results in mapped, graphical, or tabular form. Make sure also to include a discussion of where the research has taken you.
Providing an answer without taking the time to make a good discussion and/or presentation will not prove very useful. Textbooks often make the mistake of focusing on the correct numbers while failing to provide a thoughtful discussion or making a full presentation.
Hypothesis Testing: Example
Peppermint Essential Oil
Chamomile, lavender, and peppermint are some of the most popular essential oils available today. Having heard about their ability to reduce anxiety, you may want to prove whether this essential oil does indeed have some healing powers.
In this case, the hypothesis is likely to go as below:
I. The null hypothesis—As an essential oil, peppermint doesn’t assist in reducing anxiety pangs
II. The alternative hypothesis—Peppermint is capable of reducing anxiety pangs
III. Level of significance—Place the significance level at 0.25 (this will provide you with a better opportunity to prove the alternative hypothesis).
IV. P-value—It’s calculated as 0.05
V. Conclusion—Once one of the groups is proven using a placebo and the other with peppermint oil, you will need to differentiate the two according to the self-reported anxiety levels. Using your calculations, any difference that exists between the two test groups will be statistically important when it has a 0.05 p-value. This is well-below the pre-defined 0.25 alpha level. The conclusion will, therefore, note that the results of your examination support the alternative hypothesis.
TIP: Always use transition words to properly connect the sentences and paragraphs in your thesis or essay.
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Hypothesis Testing: In a Nutshell
- Sample data is used to evaluate whether a hypothesis is plausible
- Statistical analysts study hypotheses by examining and measuring randomly generated samples of the population under scrutiny
- The examination supplies evidence related to the credibility of a hypothesis based on available data.
- Hypothesis testing works by analysing statistical samples to supply evidence on whether a null hypothesis is plausible or not.
