Researchers are encouraged to report effect size statistics to quantify treatment effects or effects due to group differences. However, estimates of effect sizes, most commonly Cohen's "d," make assumptions about the distribution of data that are not always true. An alternative nonparametric estimate of effect size, relying on the median absolute deviation, is proposed. Comparison of this estimate to Cohen's "d" using (simulated) non-normally distributed data demonstrate that the nonparametric approach effect size may be a better estimate of effect size.