What statistical method should be used when comparing more than two independent groups?

The optimal method for comparing more than two independent groups is ANOVA, specifically when the assumptions of normality and homogeneity of variances are met. ANOVA is designed to evaluate differences among group means and can simultaneously test multiple groups, providing robust insights while controlling the Type I error rate that would increase with multiple t-tests.

The Friedman Test and the Kruskal-Wallis Test are non-parametric alternatives used when the assumptions of ANOVA are not met. The Friedman Test is specifically for comparing more than two related groups rather than independent groups, making it unsuitable in this context.

The Independent Samples T-Test is appropriate for comparing the means of two independent groups only, thus not applicable for scenarios involving more than two groups. Therefore, the correct statistical method for comparing more than two independent groups is ANOVA.