The Dependent Samples t-test: A Synthesis of Theory, Application, and Results

Dependent samples t-test is also called paired t-test because, the measurements of one group is paired with the measurements of the other group. Hence, repeated observation or paired observations becomes an important part of the study. Example: a patient’s health condition before the treatment and after the treatment. The unit of measure and the subject might be the same but there is a difference in time logs. 

Dependent variable groups the measureable variables like age, height, weight, temperature from different groups and then put them against each other to compare the progress. This technique to pairing the data samples and conducting dependent samples t-test have been proven to be an effective way of drawing out the cause-effect relationship between the sample groups. The difference is, dependent samples t-test just shows the difference in the means and how the variables have progressed, and not the direction in which the causality has occurred and how one variable is affecting the other. 

Before deciding to conduct dependent samples t-test on the data, ask yourself these questions to make sure you are in the right direction:

• Does each pair of observations have a direct relationship between them?
• Are the observations totally random?
• Is it necessary for both the samples to have equal number of data points?

If all the answers to the above questions turn out to be “Yes”, then the dependent samples t-test is the best fit for your data. Or else, you can use independent samples t-test.