A procedure is specified for testing the significance of predicted trends in k independent correlations. An example is also provided for illustrative purposes. (Author)

The importance of the assumption of normality for testing that a bivariate normal correlation equals zero is examined. Both empirical and theoretical evidence suggest that such tests are robust with respect to violation of the normality assumption. (Author/JKS)

The Z-variance and Box-Scheffe tests for homogeneity of variance are both relatively simple to perform and readily utilized in complex, multi-factor designs. The Z-variance test is not robust against non-normality; the Box-Scheffe test is robust against non-normality but is not nearly as powerful as the Z-variance test. (Author/BJG)

It is suggested that Levy's procedure for testing predicted trends in independent correlations should not produce grossly incorrect inferences when one is dealing with the sorts of correlations which are ordinarily encountered in empirical research. (Author/JKS)

Proposes three different multiple range tests based upon the Newman-Keuls philosophy with respect to significance levels. The three tests utilize the Fmax statistic, Cochran's statistic and a normalizing log transformation of the sample variances respectively. (Author/RC)

The purpose of this paper is to demonstrate how many more subjects are required to achieve equal power when testing certain hypotheses concerning proportions if the randomized response technique is employed for estimating a population proportion instead of the conventional technique. (Author)

The Dunnett procedure for comparing several treatment means with a control is applied to the problem of comparing several treatment variances with the variance of a control. Appropriate critical values are specified and an example is provided. (Author)

Monte Carlo techniques were employed to compare the familiar F-test with Welch's V-test procedure for testing hypotheses concerning a priori contrasts among K treatments. The two procedures were compared under homogeneous and heterogeneous variance conditions. (Author)

Hawkin's procedure for testing a sequence of observations for a shift in location could have applicability for assessing change within a single subject. Monte Carlo results suggest that Hawkins' procedure is robust with respect to moderate violations of its underlying assumptions of homogeneity of variance and normality. (Author/GDC)