The Friedman test is a nonparametric alternative to the repeated measures analysis of variance. You might use the Friedman test if you are evaluating a small sample, if your hypothesis concerns ordinal outcomes, or if you simply do not want to make the assumptions required for a repeated measures analysis of variance. The only assumptions made by the Friedman test are that the test variables are at least ordinal and that their distributions are reasonably similar.

Rather than testing the hypothesis of a difference between related ordinal variables, you may want to test a hypothesis that implies change in a binary outcome over repeated measurements or between multiple matched samples. If so, then you should consider using the Cochran Q test. Think of the Cochran Q test as an extension of the McNemar test used to assess change over two times or two matched samples. Unlike the Friedman test, the Cochran test is designed for use with binary variables.

- If you have only two samples, try the Two-Related-Samples Tests procedure.
- If your samples are not related, use the Tests for Several Independent Samples procedure.