Abstract
A fundamental goal when comparing 2 groups of participants is understanding how the groups differ. It is well known that p values are inadequate for this purpose. Numerical measures of effect size represent a step toward addressing this issue, but practical problems with the better known measures have been revealed in recent years. Kernel density estimators provide an alternative approach that is consistent with the graphical perspective initially suggested by Cohen (1977), but better known methods suffer from 2 practical problems. This article illustrates these problems and describes how they might be addressed. It is suggested that an adaptive kernel density estimator be used when summarizing data or when dealing with effect size.