Theoretically, a parametric 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-parametric test is one that makes no such assumptions. In practice, ‘parametric’ refers to tests, such as t-tests and the analysis of variance, that assume the underlying source population(s) to be normally distributed; ‘non-parametric’ refers to tests that do not make these particular assumptions. Sign test, Chi-square test, and Run test are nonparametric tests.