Distribution-free methods, also known as nonparametric methods, are statistical techniques that do not assume a specific probability distribution for the data being analyzed. These methods are particularly useful when the underlying distribution is unknown or when the sample size is too small to reliably estimate the parameters of a distribution. By relying on ranks or signs instead of raw data values, distribution-free methods can provide robust analysis in various situations where traditional parametric methods may fall short.
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