The Use (and Misuse) of Statistical Significance Testing: Some Recommendations for Improved Editorial Policy and Practice.

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 of significant results. A finding of statistical significance does not mean that the null hypothesis is false, since there are many factors affecting statistical significance such as sample size and the measurement reliability of the data. Furthermore, statistical significance alone does not permit evaluation of the importance of a finding. An effect size statistic, such as eta-squared, is more appropriate for this purpose, and editors of scholarly publications should encourage routine reporting of effect sizes. Greater reporting of nonsignificant results should also be encouraged, accompanied by power analyses to estimate Type II error. Nonsignificant results can be meaningful if the study's power to detect an effect was high. Finally, the paper emphasizes the crucial role of replication in separating true effects from Type I errors. The paper integrates analyses and criticisms of statistical practice from a variety of sources--77 references are included. (LPG)