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We use independent-samples t tests when we have two samples and different participants are in each sample. Because the samples are comprised of different people, we cannot calculate difference scores, so the comparison distribution is a distribution of differences between means. Because we are working with two separate samples of scores (rather than one set of difference scores) when we conduct an independent-samples t test, we need additional steps to calculate an estimate of spread. As part of these steps, we calculate estimates of variance from each sample, and then combine them to create a pooled variance. We can present the statistics in APA style as we did with other hypothesis tests.

As with other forms of hypothesis testing, it is useful to replace or supplement the independent-samples t test with a confidence interval. A confidence interval can be created around a difference between means using a t distribution. To understand the importance of a finding, we must also calculate an effect size. With an independent-samples t test, as with other t tests, a common effect-size measure is Cohen’s d.