Responds to Biskin's comments (this issue) on the significance test controversy. Highlights areas of agreement (importance of replication evidence, importance of effect sizes) and disagreement (influence of sample size, evaluation of populations vs. samples, significance of Carver's article). Includes further recommendations for reporting research…

The use of multiple comparisons in analysis of variance (ANOVA) is discussed. It is argued that experimentwise Type I error rate inflation can be serious and that its influences are often unnoticed in ANOVA applications. Both classical balanced omnibus and orthogonal planned contrast tests inflate experimentwise error to an identifiable maximum.…

Introduces the three subsequent articles from this special section, which extend the discussion of future prospects for progress in the reporting and interpreting of effect sizes by researchers. The authors of these pieces represent diverse views. (SLD)

The study investigated personality and aptitude correlates of test-wiseness skills, which were measured by assessing the subject's ability to utilize deductive reasoning and item cues. Personality factors and aptitude were measured. Results indicated that cognitive variables have more power to predict test-wiseness scores than do personality…

Advantages and disadvantages of three analytic methods are discussed. A hypothetical data set is employed to make the discussion complete. It is suggested that commonality analysis can be usefully employed in research studies in education, particularly when aptitute-treatment interaction studies are involved. (Author/LMO)

Comments on discussions of statistical significance testing in this special issue, placing the articles in the context of contemporary research literature. Empirical evidence is cited that suggests that the American Psychological Association's policy of encouraging effect size reporting has had no appreciable effect on the research literature.…

The relationship between analysis of variance (ANOVA) methods and their analogs (analysis of covariance and multiple analyses of variance and covariance--collectively referred to as OVA methods) and the more general analytic case is explored. A small heuristic data set is used, with a hypothetical sample of 20 subjects, randomly assigned to five…

Too few researchers understand what statistical significance testing does and does not do, and consequently their results are misinterpreted. This Digest explains the concept of statistical significance testing and discusses the meaning of probabilities, the concept of statistical significance, arguments against significance testing,…

This paper evaluates the logic underlying various criticisms of statistical significance testing and makes specific recommendations for scientific and editorial practice that might better increase the knowledge base. Reliance on the traditional hypothesis testing model has led to a major bias against nonsignificant results and to misinterpretation…