Non-parametric tests (also known as distribution-free tests) are methods of statistical analyses that do not make assumptions about the underlying distribution of the data. Most commonly, this refers to data that do not follow a normal distribution (non-normal distributions). Non-parametric tests are generally less powerful than parametric tests.

Examples of non-parametric tests include the Mann-Whitney U Test, the Kruskal-Wallis Test, and the Spearman Rank Correlation.

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