How to Use the Right Statistical Test for Hypothesis Testing?

Hypothesis Testing

Have you selected the quantitative methodology for your research work? It means that you have to conduct hypothesis testing. You’ll have to propose the hypothesis statements for testing it as well. Hypothesis testing allows you to inspect the research’s hypothesis in different ways. Hypothesis testing also helps through the provision of meaningful results. In this context you make null, and alternate hypotheses, then test their validity. You also have to accept one while rejecting the other.

Many students choose the quantitative method thinking that it is easy. But they get confused about null hypothesis at the start. When they start analysing the variables, they don’t know what the right aspect is for hypothesis testing. In this article written by experts of PhD dissertation writing services, we try to overcome this confusion. We’ll share different tests that can help students, and the researchers as well. Moreover, we’ll also explain which test is right, and in what conditions should they be applied. These tests are given as follows;

Regression:

The regression test explores relationship between two, or more variables. This means that change in one or more variables predicts change in another variable. It is important to keep in mind the following aspects;

  • A variable that causes change in other variables is the independent variable.
  • A variable that takes effect on independent variable is the dependent variable.

It is important to keep in mind that these variables don’t have a causal relationship. We have to test whether one predicts the other or not. It doesn’t mean that one causes the other. So we can say that “X” predicts “Y”, but we can’t ever say that “X” causes “Y”. This test is appropriate for usage when you observe the predictive relationship between variables.

Correlation:

We use a correlation test for observing the relationship between two variables. It doesn’t matter whether the relationship is causal or not. Because these variables have a linear relationship between them. It means that they change together at the same rate. Correlation is a common tool for observing the relationship between these variables. For hypothesis testing, you don’t need to make a statement about cause and effect. For example, there is a correlation between the heights of parents and their children. This test is best suited for when you want to see the linear relationship between two variables. You don’t have to define cause in your hypothesis either. You only have to observe the relationship in hypothesis testing.

Chi-square:

Chi-square test is a way of checking the relationship between categorical variables. This test tells about the difference that exists between observed, and expected values. The main idea behind the test is to compare observed and expected values if the null hypothesis is true. Chi-square is best for hypothesis testing when it comes to observing the relationship between categorical variables. It is also the right type of test for identifying the differences between observed, and expected values.

T-Test:

T-test is a statistical test for comparing the means of two groups. It also identifies whether the two groups are same, or different from each other. You don’t compare more than two means in the t-test. This is because t-test assumes that your data is independent. Moreover, it also assumes that both groups have the same amount of variance. T-test also highlights whether the two groups are significant or not. In short, this test is the right option for hypothesis testing when you compare the different means. Moreover, it is suitable when your sample size is less than 30, and the standard deviation is unknown.

Z-Test:

Z-test is a way of identifying the differences between two population means. Moreover, the variance is also given. The required sample size of population for this test is larger than 30. Z-test follows normal distribution for hypothesis testing. It has been found that Z-test and t-test have many similarities. When it comes to t-test, the standard deviation aspect is not known. Whereas Z-test is right to use when you have a large sample. The aspect of standard deviation is also provided in its case. Apart from this one shortcoming, t-test is a better option than z-test.

ANOVA:

When you have to compare more than two means, especially in Bibliography, ANOVA is the better option. ANOVA separates observed variance data for further tests. It is important to highlight that there are two types of ANOVA. These include one-way, and two-way ANOVA. ANOVA test allows you to observe the relationship between more than two groups. Hence ANOVA is the right choice for comparing more than two groups of data.

Author: Albert Barkley

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