A generally robust approach to hypothesis testing in independent and correlated groups designs.

Standard least squares analysis of variance methods suffer from poor power under arbitrarily small departures from normality and fail to control the probability of a Type I error when standard assumptions are violated. These problems are vastly reduced when using a robust measure of location; incorporating bootstrap methods can result in additional benefits. This paper illustrates the use of trimmed means with an approximate degrees of freedom heteroskedastic statistic for independent and correlated groups designs in order to achieve robustness to the biasing effects of nonnormality and variance heterogeneity. As well, we indicate when a boostrap methodology can be effectively employed to provide improved Type I error control. We also illustrate, with examples from the psychophysiological literature, the use of a new computer program to obtain numerical results for these solutions.