  In many biological applications, the primary objective of study is to
quantify the magnitude of treatment effect between two groups. Cohens'd or
strictly standardized mean difference (SSMD) can be used to measure effect size
however, it is sensitive to violation of assumption of normality. Here, we
propose an alternative metric of standardized effect size measure to improve
robustness and interpretability, based on the overlap between two sample
distributions. The proposed method is a non-parametric generalized variant of
SSMD (Strictly Standardized Mean Difference). We characterized proposed measure
in various simulation settings to illustrate its behavior. We also investigated
finite sample properties on the estimation of effect size and draw some
guidelines. As a case study, we applied our measure for hit selection problem
in an RNAi experiment and showed superiority of proposed method.
