Comments on the paper ‘Type I error and test power of different tests for testing interaction effects in factorial experiments’ by M. Mendes and S. Yigit (Statistica Neerlandica, 2013, pp. 1–26)

We give some comments on the paper by Mendes and Yigit where some misleading statements on rank tests have been given. Also, we give some additional important references on the same topic, which are not cited in this paper. By extending the simulations presented in the Mendes and Yigit paper to larger and unequal sample sizes, we demonstrate that the main conclusions are misleading.     

Comment on ‘Type I error and test power of different tests for testing interaction effects in factorial experiments’

We discuss the rank transform method and particularly the hypotheses being tested. Some counter examples show that the rank transformation method is invalid in factorial designs.     

Type I error and test power of different tests for testing interaction effects in factorial experiments

A simulation study was conducted to investigate the effect of non normality and unequal variances on Type I error rates and test power of the classical factorial anova F‐test and different alternatives, namely rank transformation procedure (FR), winsorized mean (FW), modified mean (FM) and permutation test (FP) for testing interaction effects. Simulation results showed that as long as no significant deviation from normality and homogeneity of the variances exists, generally all of the tests displayed similar results. However, if there is significant deviation from the assumptions, the other tests are observed to be affected at considerably high levels except FR and FP tests. As a result, when the assumptions of factorial anova F‐test are not met or, in the case those assumptions are not tested whether met, it can be concluded that using FR and FP tests is more suitable than the classical factorial anova F‐test.