The concept of statistical significance is explained, with specific numerical illustrations. (MNS)

A dialog between two fictional teachers provides some basic examples of how research that uses approved methodology may provide results that are significant statistically but not significant practically. (PGD)

The second half of a dialogue between two fictional teachers examines the significance of statistical significance in research and considers the factors affecting the extent to which research results provide important or useful information. (PGD)

Although the primary responsibility rests with the authors of articles reporting nonsignificant results to demonstrate the worth of the results by discussing the power of the tests, consumers should be prepared to conduct their own power analyses. This article demonstrates the use of power analysis for the interpretation of nonsignificant…

An explanation of the misuse of statistical significance testing and the true meaning of "significance" is offered. Literature about the criticism of current practices of researchers and publications is reviewed in the context of tests of significance. The problem under consideration occurs when researchers attempt to do more than just establish…

Power is an indicator of the ability of a statistical analysis to detect a phenomenon that does in fact exist. The issue of power is crucial for social science research because sample size, effects, and relationships studied tend to be small and the power of a study relates directly to the size of the effect of interest and the sample size.…

Measures of effect size were computed for 237 statistical tests from 59 early intervention studies. Data revealed that the average treatment effect across studies was medium in size. Interpretation of measures of magnitude strength is discussed in relation to statistical significance testing. Reporting of measures of effect size along with…

In the educational research literature alpha, the a priori level of significance, and p, the a posteriori probability of obtaining a test statistic of at least a certain value when the null hypothesis is true, are often confused. Explanations for this confusion are offered. Paradoxically, alpha retains a prominent place in textbook discussions of…

This commentary on EC 603 695 argues that significance testing is a necessary but insufficient condition for positivistic research, that judgment-based assessment and single-subject research are not substitutes for significance testing, and that sampling fluctuation should be considered as one of numerous epistemological concerns in any…

This paper reviews criticism on misinterpretation and overuse of significance testing in the social sciences and examines use of effect size measures to enhance interpretation of significance testing. Review of typical effect size measures and their application is followed by analysis of use of effect size measures in studies reported over 10…

Using a Monte Carlo approach, comparison was made between traditional procedures and a multiple linear regression approach to test for differences between values of r sub 1 and r sub 2 when sample data were dependent and independent. For independent sample data, results from a z-test were compared to results from using multiple linear regression.…

This article compares three models that have been the foundation for approaches to the analysis of statistical significance in early childhood research--the Fisherian and the Neyman-Pearson models (both considered "classical" approaches), and the Bayesian model. The article concludes that all three models have a place in the analysis of research…

This article explains and illustrates the estimation of the power of statistical tests used to analyze data in early childhood special education research, and discusses advantages and disadvantages of various ways to increase power, such as using a directional alternate hypothesis or using a parametric, rather than nonparametric, statistical test.…

This tutorial summarizes some of the widely known limitations of tests of statistical significance and then focuses on extracting measures of variation accounted for as a supplement to significance testing. Two measures of variation accounted for, eta squared and omega squared, are discussed. Computational formulas, computational examples, and…

Measurement of clinically significant change is critical for rehabilitation research because it can enhance the credibility of rehabilitation efforts and guide evidence-based practices. The practical appeal of clinically significant change is that it can bridge research and clinical practice by focusing on individual rather than group differences.…