Hypothesis testing is a statistical technique used for deciding whether to reject the null hypothesis. Hypothesis testing helps researchers know whether or not they are on the correct path for their research project and can save them from time wasted pursuing wrong leads. Before starting any type of hypothesis test, it is vital that you have an idea of what your variable will be and how to measure it accurately. This post will cover some of the most common types of hypothesis tests and provide examples, as well as discuss what they are designed to test.
Hypothesis in research is basically a statement that helps you to define the relationship between different types of variables for your study. You can consider the hypothesis as the expectation about the things that can happen during research. The main objective of including hypotheses in research is to get the answer to research questions.
For example, while watering a plant you expect that if you will give more amount of water and sunlight to the plant, it would grow big soon.
There are different types of hypothesis but here, we will discuss mainly two types of hypothesis, these are :
- Null hypothesis: It is a hypothesis, where you can not expect variations. The null hypothesis states that there is no relationship between the dependent and independent variables. For instance, there is no significant relationship between compensation policy and employee satisfaction. In research null hypothesis is denoted as H0.
- Alternative hypothesis: In the Alternative hypothesis, variations are expected. This is something that a researcher aims to indirectly verify by stating their assumption, which states that there exists a significant linkage between population parameters. For instance, there is a significant relationship between companies’ compensation policy and employee satisfaction. It denoted as Ha or H1.
Hypothesis testing is basically a statistical procedure that is researcher performs with the purpose of determining whether there are chances of a specific hypothesis to be true. In simple words, by using the statistics you can tests whether your prediction about the population parameters is correct or not.
Or we can say, Hypothesis testing is a research method that uses statistical tools to prove or disprove a theory. The hypothesis is an idea about what might be true, and the goal of hypothesis testing is to provide evidence for or against that idea.
The test procedure or the rule is based upon a test statistic and a rejection region. The process of testing the hypothesis consists of the following steps:
It is the first step in hypothesis testing where you need to clearly define the null and alternative hypotheses. While stating the hypothesis you need to make sure that it is mutually exclusive which means that if one statement is true then the other should be false. While defining the variables you need to confirm that the statement representing the relationship between two or more variables.
This is a stage where you need to set the significance level. The significance level (denoted by the Greek letter alpha— a) is generally set at 0.05. This means that there is a 5% chance that you will accept your alternative hypothesis when your null hypothesis is actually true. The smaller the significance level, the greater the burden of proof needed to reject the null hypothesis, or in other words, to support the alternative hypothesis.
It is a stage where you will require accumulating all the facts about the research topic. The process of making statistical inferences should be done in a way that is designed to test your hypothesis. If you do not design the sampling methods and data collection appropriately, then it will make no sense for you to try drawing conclusions about any population at all!
This is a phase where you need to determine P-value. It is basically a value that the researcher utilizes for determining statistical importance in hypothesis tests. Determination of P-value is important as it will help you in evaluating the extent up to which the hypothesis statement given by you is true. It will also help you in analyzing the extent up to which the facts which you have gathered are compatible with the null hypothesis.
There are basically two types of P-value these are :
Comparison of critical value and making a judgment: Here, you need to make a comparison between P values, and on the basis of the same you need to make a decision.
With the conclusion stage, we either accept or reject the null hypothesis. The decision is based on computed values of the test statistics and whether it lies in the acceptance region or rejection region respectively.
If the computed value of the test statistic falls in the acceptance region (it means the computed value is less than the critical value), the null hypothesis is accepted. On the contrary, if the computed value of the test statistic is greater than the critical value, the computed value of the statistic falls in the rejection region, and the null hypothesis is rejected.
A manager in pipe manufacturing needs to ensure that the diameters of pipes manufactured by the machine are 5 cm. Then you as a manager is needed to undergo the following phases of the hypothesis test these are:
Read Also: Thematic Analysis for Research
UP TO 25 % DISCOUNT
Get Your Research Paper Completed At Lower Prices