The most commonly used statistical tests (t-tests, Z-tests, ANOVA, etc.) are based on a number of assumptions (see testing assumptions above). Non-parametric tests, while not assumption-free, make no assumption of a specific distribution for the population. The qualifiers (assuming ...) for non-parametric tests are always much less restrictive than for their parametric counterparts. For example, classical ANOVA requires the assumptions of mutually independent random samples drawn from normal distributions that have equal variances, while the non-parametric counterparts require only the assumption that the samples come from any identical continuous distributions. Also, classical statistical methods are strictly valid ...

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