Non-Parametric Test
Non-parametric tests are statistical methods used to analyze data that do not meet the assumptions of parametric tests, such as normality and homogeneity of variance. These tests are also known as distribution-free tests, as they do not require any assumptions about the underlying distribution of the data. Non-parametric tests are often used when the data is ordinal, nominal, or skewed. These tests are less powerful than parametric tests, but they are more robust and can be used in a wider range of situations. Some examples of non-parametric tests include the Wilcoxon rank-sum test, Kruskal-Wallis test, and Mann-Whitney U test.
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