The researcher is responsible for reviewing the assumptions pertinent to the chosen test and
performing diagnostic checks on the data to ensure the selection’s appropriateness . . . Parametric tests place different emphases on the importance of assumptions. Some tests are quite robust and hold up well despite violations. With others, a departure from linearity or equality of variance may threaten result validity. Nonparametric tests have fewer and less
stringent assumptions. They do not specify normally distributed populations or homogeneity
of variance. Some tests require independent cases while others are expressly designed for
situations with related cases (Emory and Cooper, 1991).