|In the literal meaning of the terms, a parametric statistical test is one that makes assumptions about the parameters (defining properties) of the population distribution(s) from which one's data are drawn, while a non-|
For practical purposes, you can think of "parametric" as referring to tests, such as t-tests and the analysis of variance, that assume the underlying source population(s) to be normally distributed; they generally also assume that one's measures derive from an equal-
the Fisher Exact Probability test (Subchapter 8a),
the Wilcoxon Signed-
the Kruskal-Wallis Test (Subchapter 14a),
and the Friedman Test (Subchapter 15a).