In hierarchical-design experiments researchers need to test for nested effects, but sometimes parametric assumptions are violated. We show that the Kruskal-Wallis (K-W) rank test can be extended to test for such effects. Let B = b1 . . . bh be the tested effect,and A = a1 . . . ag be the effect immediately above it in the hierarchy. If one calculates separate K-W statistics for the effect B at each level of A