Since the 1940s the technique of mixing indices has been used to determine the level of inhomogeneity in powder mixtures. In 1995, the application of the analysis of variance technique (ANOVA) to this problem was introduced and was shown to overcome several critical limitations of the index approach. However, one possible limitation of ANOVA is its normality assumption. Thus, this article examines the robustness of this assumption using several very non-normal distributions for measurement errors and compares the ANOVA technique with the nonparametric Kruskal-Wallis method in a Monte Carlo simulation study. The Kruskal-Wallis method makes no assumption regarding the distribution of the data.