There is a need for effect sizes that are readily interpretable by a broad audience. One index that might fill this need is [pi], which represents the proportion of scores in one group that exceed the mean of another group. The robustness of estimates of [pi] to violations of normality had not been explored. Using simulated data, three estimates…

Publication bias arises when studies with favorable results are more likely to be reported than are studies with null findings. If this bias occurs in studies with single-subject experimental designs (SSEDs) on applied behavior-analytic (ABA) interventions, it could lead to exaggerated estimates of intervention effects. Therefore, we conducted an…

This comment offers three descriptions of "p[subscript rep]" that start with a frequentist account of confidence intervals, draw on R. A. Fisher's fiducial argument, and do not make Bayesian assumptions. Links are described among "p[subscript rep]," "p" values, and the probability a confidence interval will capture…

Approximations to the distribution of a common form of effect size are presented. Single sample tests, confidence interval formulation, tests of homogeneity, and pooling procedures are based on these approximations. Caveats are presented concerning statistical procedures as applied to sample effect sizes commonly used in meta-analysis. (Author)

The use of p-values in combining results of studies often involves studies that are potentially aberrant. This paper proposes a combined test that permits trimming some of the extreme p-values. The trimmed statistic is based on an inverse cumulative normal transformation of the ordered p-values. (SLD)

The second approach used by L. V. Hedges and L. Friedman (1993) in their reanalysis of Feingold's earlier results is conceptually equivalent and yields results consistent with the original interpretation. The first method yields results that are discrepant. Analyses of variations in tail effect sizes are planned. (SLD)

Feingold's reply illustrates that his steps in characterizing tail effect sizes are not the calculations the authors had imagined. Attempting to reproduce Feingold's calculations, the authors still often find themselves in disagreement with interpretations Feingold has placed in his table. (SLD)

Analyzes effect sizes in tails of distribution of scores in Feingold's study of joint effects of gender differences in mean and variability on 28 cognitive-ability scales. Effect sizes are smaller than Feingold assumed. Evaluates joint effect of gender differences by number of males and females in extreme score ranges. (SLD)