Non-Parametric Significance Tests
The benefit of non-parametric tests over parametric tests is that they not make any assumptions about the data. Thus, they are well-suited in situations where the assumptions of parametric tests are not met, which is typically the case for small sample sizes.
Popular non-parametric test
This table gives an overview over popular non-parametric tests:
| Test | Test for what? |
|---|---|
| Wilcoxon rank sum test | Difference in medians |
| Wilcoxon signed-rank test | Difference in paired means |
| Fisher’s exact test | Independence in contingency tables |
| Kruskal-Wallis test | Difference of multiple medians |
