Effect sizes are an important complement to null hypothesis significance testing (e.g., p-values), in that they offer a measure of practical significance in terms of the magnitude of the effect, and are independent of sample size. As an additional benefit, dimensionless, or standardized measures of effect size allow direct comparison of two or more quantities, for example variables measured on different scales or independent studies in a meta-analysis. The statistical community has long encouraged researchers to report effect sizes (Wilkinson and Task Force on Statistical Inference, 1999; Kline, 2004; Nakagawa and Cuthill, 2007), and scientific journals are increasingly requesting or requiring authors to report them along with p-values (e.g., Snyder, 2000; Huberty, 2002; Fidler et al., 2005).
