Detecting assumption violations in mixed-model analysis of variance.

Parametric analysis of variance (ANOVA) is frequently used to analyse experimental data, yet for the results to be considered as accurate, certain assumptions must be respected: the normality of the distribution of the sampled data, the homogeneity of variance among the groups being compared (i.e., homoscedasticity), and, in certain cases, sphericity. The present work focuses on the methods for detecting violations of these assumptions and provides an example of the application of these methods.