This article summarizes and scrutinizes the growth of the development of clinically relevant and psychometrically sound approaches for determining the clinical significance of treatment effects in mental health research by tracing its evolution, by examining modifications in the method, and by discussing representative applications. Future directions for this methodology are proposed. Jacobson, Follette, and Revenstorf (1984) proposed one of several methods for determining the practical importance of statistical effects found in clinical trials. In that article, as well as in subsequent publications , Jacobson and colleagues attempted to grapple with two limitations prevalent in statistical comparisons between groups of treated clients. First, such comparisons provide little or no information regarding the variability in treatment response from person to person. Group means, for example, do not in and of themselves indicate the proportion of participants who have improved or recovered as a result of treatment. Thus, statistical comparisons between groups shed little light on the proportion of participants in each condition who have benefited from the treatment. Second, standard statistical comparisons between groups seldom determine the practical importance of the treatment effects. Although previous investigators had tried to improve on standard statistical comparisons by reporting the size of the statistical effect (e.g., Smith, Glass, & Miller, 1980), even effect sizes do not directly speak to clinical significance. Although large effects are more likely to be clinically significant than small ones, even large effects can be clinically insignificant.There are a multitude of ways that one can characterize variability in treatment response and at least as many ways that one can determine whether changes are clinically significant (see articles in this special section). However the concept is defined, there are a variety of ways to operationalize clinical significance in mathematical terms. It is possible to distinguish the conceptual definition from its mathematical interpretation. Each has its own areas of controversy, and some of the mathematical debate is quite esoteric. Moreover, the mathematical distinctions between the original metric (Jacobson, Follette, & Revenstorf, 1984) and more recent alternatives are all based on assumptions that cannot be tested without an empirical comparison of the methods. The ulti-