Calculating and reporting appropriate measures of effect size are becoming standard practice in psychological research. One of the most common scenarios encountered involves the comparison of 2 groups, which includes research designs that are experimental (e.g., random assignment to treatment vs. placebo conditions) and nonexperimental (e.g., testing for gender differences). Familiar measures such as the standardized mean difference (d) or the point-biserial correlation (rpb) characterize the magnitude of the difference between groups, but these effect size measures are sensitive to a number of additional influences. For example, R. E. McGrath and G. J. Meyer (2006) showed that rpb is sensitive to sample base rates, and extending their analysis to situations of unequal variances reveals that d is, too. The probability-based measure A, the nonparametric generalization of what K. O. McGraw and S. P. Wong (1992) called the common language effect size statistic, is insensitive to base rates and more robust to several other factors (e.g., extreme scores, nonlinear transformations). In addition to its excellent generalizability across contexts, A is easy to understand and can be obtained from standard computer output or through simple hand calculations.