Common Statistical Tests Used In A Research Study

Common Statistical Tests
Statistics are the arrangement of statistical tests to get useful information from the given data. These common statistical tests help analysts observe different patterns and relations. The analysts then make decisions based on those patterns and relations. There are several statistical tests that analysts can use in their dissertation research and analysis. But some of them are common statistical tests that every researcher can use. The choice of a test depends on the type of data, distribution of the data, and variable types.

Choosing a Statistical Test

There are many statistical tests like t-test, z-test, or ANOVA test. The choice of a test depends on certain factors. I have described those factors in the paragraph above. The choice of common statistical tests depend on the type of data distribution. If the data is normally distributed, the analysts use parametric tests. The parametric tests are also the most commonly used tests in statistical analysis. There are further three types of parametric tests which are as follows;


The first common statistical test is the T-test. In the T-test, the researcher compares the mean of the two variables. The analysts use this test when population parameters are unknown. Now, what are the population parameters? The population parameters are the mean and standard deviation. There are three further types of T-tests. The description of those tests is as follows;

Paired T-test

The analysts use this test to compare two variables from the same population. It includes pre and post-scores of the population. For example, in a training program, the analysts give scores to the participants. The researcher will use paired t-test to analyze the scores before and after the program.

Independent T-test

This test determines the statistical difference between two unrelated groups. It is also called a two-sample t-test and a students’ t-test. The typical example of this test is comparing boys and girls in a population.

One sample T-test

Among these types of common statistical tests, the researcher compares the mean of a group with the given mean. One typical example of this is to check the increase and decrease in the sales with an average sale already given.

ANOVA test

Analysis of variance (ANOVA) is a very common statistical test. The analysts use this to check the difference between the means of two or more groups. This test checks the effect of different factors on the means of different samples. The number of samples in this test is always more than two. Therefore, we can’t use a t-test instead of an ANOVA.

The ANOVA tests the null hypothesis that all means of the samples are the same. The research question in the ANOVA is very clear. It is “Is there a difference between the means of our sample groups?” There are assumptions that you need to make before applying the ANOVA test. These assumptions are as follows;
  • The variance of all the population samples is the same
  • The population sample of each group is normally distributed
  • The researcher has made observations randomly
  • Every sample is independent of the other
Once you have made these assumptions, you’re good to go.

Correlation and Regression Test

Among most common statistical tests is the regression and correlation test. It relates to the fundamental goal of statistics, i.e., to identify relationships. In the correlation test, the two variables are always symmetric. It doesn’t matter which one is dependent and which is independent. But it does matter in regression tests. In regression, one variable is designated as dependent and the other as independent. The researcher sometimes calls the independent variable an explanatory variable. But in a multiple regression test, there will be more than one independent variable. Regression is also the most common statistical test that analysts use.

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In regression, there will be a correlation coefficient. The correlation coefficient measures the level of relationship between the two variables. Pearson coefficient is one of the most common correlation coefficients. The regression analysis provides you with an equation. The researchers use that equation to make predictions about the data. There is no minimum sample size limitation for regression analysis. You just need to have a dependent and independent variable.


Running common statistical tests require both skills and energy. Sometimes, you’ll put in all the information and find nothing. The researchers should be crystal clear about all the variables in the data. Therefore, you’ll have to equip yourself with strong and relevant data to establish a relationship.

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