When Should You Use an Independent Samples t-Test?

Learn the assumptions and interpretation of the independent samples t-test, used to compare the means of two independent groups.

When is this test used?

It’s used when you want to compare the means of a continuous variable between two independent groups — for example, a “treatment” and a “control” group, or male and female participants. A typical use case: comparing exam scores between two different teaching methods.

Assumptions

• The dependent variable must be continuous and approximately normally distributed within each group.
• Observations must be independent of one another (the same participant cannot appear in both groups).
• The variances of the two groups should be roughly equal (if not, a corrected version such as Welch’s t-test is used instead).

How to interpret the result

If the p-value falls below your chosen significance level (usually 0.05), you conclude there’s a statistically significant difference between the two group means. Alongside significance, you should also report the size of that difference using an effect-size measure such as Cohen’s d.

Example: Comparing exam averages between students who took a course online versus in person, if you find t(58) = 2.14, p = 0.037, there’s a significant difference between the groups at the 95% confidence level.

If the assumptions aren’t met (for example, the data isn’t normally distributed), the non-parametric alternative — the Mann-Whitney U test — can be used instead.