Over the years, methodologists have been recommending that researchers use magnitude of effect estimates in result interpretation to highlight the distinction between statistical and practical significance (cf. R. Kirk, 1996). A magnitude of effect statistic (i.e., effect size) tells to what degree the dependent variable can be controlled, predicted, or explained by the independent variable (P. Snyder and S. Lawson, 1993). There are a number of ways one can compute an effect size statistic as part of data analysis. There is no concept of "one size fits all" (B. Thompson, 1999), so it is up to the smart researcher to choose the index best suited for a particular research endeavor. It has now become necessary that such a statistic always be included to enable other researchers to carry out meta-analyses and to inform judgment regarding the practical significance of results. This paper provides a tutorial summary of some of the effect size choices so that researchers will be able to follow the recommendations of the American Psychological Association (APA) publication manual, those of the APA Task Force on Statistical Inference, and the publication requirements of some journals. (Contains 3 tables and 11 references.) (Author/SLD)