Paired t-Test calculator

To calculate a paired samples t-test online (dependent samples t-test), simply select two metric variables and click on Paired t-test. After that, a t-test for paired samples is automatically calculated online.

Descriptive statistics and test statistics, such as the t-value and p-value for the paired t-test, are then calculated and clearly displayed.

Paired samples t-Test

A paired samples t-test, also known as a dependent samples t-test or a paired t-test, is a statistical test used to compare the means of two related groups. The groups are "paired" because they are somehow related or matched. This test is appropriate when you have two sets of related data points and you want to determine whether there's a significant difference in the means of these two sets.

Common scenarios where a paired samples t-test might be used include:

• Before-and-after comparisons: For example, measuring the blood pressure of a group of patients before and after a certain treatment.
• Repeated measures: Such as measuring a particular variable in the same subjects under two different conditions.
• Matched pairs: When each subject in one group is matched with a subject in another group based on certain criteria (e.g., age, gender).

The paired t-test works by first computing the difference between each pair of observations. Then, the mean and standard deviation of these differences are calculated. The t-statistic is computed based on the mean difference and the standard deviation of the differences, and this statistic is used to determine if the difference between the two sets of measurements is statistically significant.

In essence, the paired samples t-test tells you whether the mean difference between paired observations is significantly different from zero (or any other specified value). If you have more than two paired samples, you can calculate the analysis of variance for repeated measures online.

Paired samples t-Test assumptions

To perform a paired samples t-test, certain assumptions should be met:

• Normality: The differences between the paired observations should be approximately normally distributed.
• Independence: The paired observations should be independent of each other.

If the assumptions are not met, alternative non-parametric tests, such as the Wilcoxon signed-rank test, might be more appropriate.