J. P. Shaffer has presented two tests to improve the power of multiple comparison procedures. This article described an algorithm to carry out the tests. The logic of the algorithm and an application to a data set are given. (SLD)

Results of a Monte Carlo study with 4 populations (3,072 conditions) indicate that when distributions depart markedly from normality, nonparametric analysis and parametric analysis of transformed data show superior power to parametric analysis of raw data. Under conditions studied, parametric analysis of transformed data is more powerful than…

Neglected issues of simultaneous statistical inference and statistical power in survey research applications of the general linear model are reviewed, and it was found that classical hypothesis testing as it is currently applied, is inadequate for the purposes of social research. The intelligent use of statistical inference demands control over…

Stability of Type I error rates and power are investigated for three forms of the Box test and two forms of the jackknife test with equal and unequal sample sizes under conditions of normality and nonnormality. The Box test is shown to be robust to violations of the assumption of normality when sampling is from leptokurtic populations. The…

Five distribution-free alternatives to parametric analysis of covariance are presented and demonstrated: Quade's distribution-free test, Puri and Sen's solution, McSweeney and Porter's rank transformation, Burnett and Barr's rank difference scores, and Shirley's general linear model solution. The results of simulation studies regarding Type I…