Non-parametric
Non-parametric refers to statistical methods that do not assume a specific probability distribution for the population being analyzed. These methods are often used when the data does not meet the assumptions of parametric tests, such as normality or equal variance. Non-parametric methods are also useful when the sample size is small or when the data is ordinal or categorical. Examples of non-parametric tests include the Wilcoxon rank-sum test, Kruskal-Wallis test, and Mann-Whitney U test. Non-parametric methods can also be used for regression analysis, such as the local regression method known as LOESS. Non-parametric methods are often considered more robust than parametric methods, as they do not rely on assumptions about the underlying distribution of the data.
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