Non-parametric tests, also sometimes called distribution free tests, are a type of statistical test which is necessary to use when data does not have a probability distribution or does not have a distribution's parameters specified and known. While technically non-parametric tests can be used on all kinds of data including even data with a normal distribution, usually parametric tests are preferred due to issues of statistical power.
Non-parametric tests include:
- Spearman’s rank correlation (similar to the Pearson's correlation)
- Kruskal-Wallis test (similar to the ANOVA )
- Wilcoxon signed-rank test (similar to the paired T test )
- Mann–Whitney–Wilcoxon test (similar to the two sample T test )
- Pearson's chi squared test
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