Distribution-free tests, also known as nonparametric tests, are statistical methods that do not assume a specific distribution for the data being analyzed. These tests are particularly useful when the data do not meet the assumptions required for parametric tests, such as normality. Because they rely on ranks or signs rather than specific data values, distribution-free tests provide a robust alternative for assessing location and scale without requiring stringent assumptions about the underlying population distribution.
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