Statistical significance and practical significance can be considered jointly through the use of non-nil null hypotheses that are based on values deemed to be practically significant. When examining differences between the means of two groups, researchers can use a randomization test or an independent t test. The issue addressed in this paper is…

For years, researchers have debated the misinterpretation of the null hypothesis significance test (NHST). Many researchers overemphasize the results of the NHST and underemphasize or even omit effect size measures. This paper addresses the common mistaken perceptions regarding the NHST. Several common effect size estimates are discussed. A small…

The Wilcoxon two-sample test used to examine judge bias. (MM)

This paper provides the computational formulas necessary for testing the significance of the difference between mean values of two bivariate normal populations. (Author)

It is suggested that the binomial theorem be used to compute the probability that a given number of Type I errors would occur when a group of null hypotheses are true and that this result be used as the level of significance for the test of an overall hypothesis. (Author/BW)

Criticizes an article in which S. A. Cohen and J. S. Hyman describe as "suspicious" the high proportion of statistically significant findings reported in published educational research journals and offers alternative explanations for the high statistical power reported. (GC)

A Bayesian solution is presented for the Johnson-Neyman problem (whether or not the distance between two regression lines is statistically significant over a finite interval of the independent variable). (Author/CTM)

A study was conducted using generated data sets that contained specified amounts of error to determine empirically which of two large sample approximations for the coefficient of concordance or weighted average tau was more appropriate for various numbers of judges and numbers of objects. (CTM)

Two commentaries describe some shortcomings of a recent discussion of the significance testing of R-squared by C. J. Huberty and upward bias in the statistic. Both propose some modifications. A response by Huberty acknowledges the importance of the exchange of ideas in the field of data analysis. (SLD)

Identifies the common supposition in research by business educators that a research outcome that is statistically significant is necessarily practically significant. Describes the use and interpretation of a simple objective measure of practical significance, called Effect Size. (Author)

Contends that tests of statistical significance and measures of magnitude in counseling psychology research do not provide same information. Argues interpreting magnitude of experimental effects must be two-stage decision process with the second stage of process being conditioned on results of a test of statistical significance and entailing…

The estimate-and-subtract method for eliminating threats to validity is described. It is argued that the method is superior to the use of no-difference findings for this purpose. Two ways of improving the no-difference findings are presented. (TJH)

Investigates the presence of clustering structure in a document collection and the influence of the presence of clustering structure on the success of cluster-based retrieval. Term-weight and similarity thresholds are discussed, empirical and statistical significance are considered, and indexing exhaustivity for document representation is…