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5 Most Common Statistical Tests in Research Papers for Data Analysis

There are many statistical tests you can use in your paper for data analysis. However, it is very important that you choose the right method for your study. Thus, you need to have an understanding of some of the common statistical methods used for testing. This article will give you five common statistical tests in the research paper for data analysis.

1. T-Tests:

The first one of the most common statistical tests that you can you use for data analysis is the T-test. Paired t-test is usually used to make a comparison with 2 variables in the same population. For instance, a post and pre-score from the same population are taken, or a score under different conditions from same the same group is taken. The aim of this test is to ensure that there is some evidence that the mean difference of different outcomes is different from 0 or not.

These statistical tests are usually referred to as repeated or dependent t-tests. To use this test properly, you need to make a certain assumptions for your data, such as the dependent variables must need to be continuous, subjects for each situation must need to be the same, and the difference in the pair values must be distributed in a normal way. The T-test can help you to look at the differences between the 2 different groups. In simple words, this method helps to compare the means of two groups.

In this test, you need to assume that the dependent variable is continuous and the independent one is categorical. In the samples, there is no relation between the subjects. In samples, the independent variable is distributed in a normal way, and the variance is the same in all groups. Still, if you can’t do this on your own, you may need to get dissertation help online.

2. Analysis of Variance (ANOVA):

Another one of the commonly used statistician tests is the analysis of variance. There are many limitations in the above method of t-test for data analysis. You can compare only two means, and these two means can be used with just one variable in the above test. However, ANOVA (analysis of variance) can compare several means. These means can be used with multiple independent variables in ANOVA. In this method, you will test the null hypothesis, which is that all means have the same value.

The research question that we will ask with this method is that “is there any difference in the means of the groups of the study after a certain treatment?” Thus, the statistical question in this is that “do the means different from what we would expect if the null hypothesis is correct? “. The statistic used for this method of testing is the F-statistic. In this method, you will compare the variation between the means of the sample to the variation in all samples. All of this will be equal under the null hypothesis.

In such statistics tests, you will make assumptions such as every group is distributed normally throughout the population, there will be a common variance in all populations, samples will be drawn independently, and there will be random observations for samples.

3. Regression:

When it comes to statistical tests, regression is a very common method. Regression is the relation between an independent variable and the dependent variable. It can be explained in terms of causes and effects means how one variable can impact another one or how changes in one variable can cause the change in another variable. In this method, you will use graphs and charts to show the results. The line used in charts or graphs will show how strong or weak there is a relationship between the dependent and independent variables. It will also show some trends and patterns for a particular period of time.

These tests are used in data analysis to forecast trends and make predictions. For instance, you might use regression to check how a particular service or product might sell to the customers. Regression can be used to predict what organic traffic will look like in the next six months.

4. Nonparametric Tests:

Another one of the most common statistical tests is nonparametric tests. In a parametric test, you first need to ensure that you have met all the assumptions while designing the research study and collecting the data. It is because parametric tests are very powerful tests. We cannot use this test if we are unable to meet the assumptions required for the study. You use statistical software to run this test. This includes the following steps:

  • The statistic of the test is determined. Usually, statistics explains how much the relations between the variables are different from the null hypothesis,
  • After that, the p-value is determined
  • There will be a strong relationship if the value of the statistics is very different from the value of the null hypothesis.

After completing the data analysis, you will include all your results in your paper and report your findings.

5. Sample size determination:

When it comes to data analysis by using statistical tests, a dataset simple can be very large sometimes. This can make it hard for you to collect data from the dataset for each element. In this case, you can go to the route of the analysis of your sample size. This process is usually called sample size determination. To do this in the correct way, you should calculate the rights size of your sample. If your sample is small, then you will not get correct outcomes at the end of your data analysis. On the other hand, if your sample size is very large, then you can waste a lot of money and time calculating the results from the sample. You need to examine various aspects to determine the proper size of a sample, such as time, money or other factors to collect data.


The above guide can help you get to understand the most common statistical tests used for data analysis in research papers. You can use one of the five above-given methods to analyze the data of your study.

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