This article addresses the design and selection of outcome methods for treatment research from a statistical perspective. Two of the major statistical and methodological issues relevant to the selection of dependent variable(s) are statistical power and social utility. Power is the paramount issue in research design. Power can be improved by measures and methods tailored to the predicted effects of treatment. These effects should vary from treatment to treatment, suggesting that no single outcome measure is suitable for all purposes. In estimating power, researchers should also consider the time-varying nature of most types of treatment effects. Given these considerations, timeline procedures have advantages because they allow the derivation of measures linked to specific treatment components. They also permit the study of time-varying effects of variables such as life stress. An analysis on the relationship between drinking and self-reported stress is presented as an illustration of how advanced statistical methods, in conjunction with carefully gathered data, can provide process data on how treatment can affect individual responses to stressors or other events. From a social utility perspective, however, having research focus on narrower and narrower slices of behavior in the search for power raises questions about the value of this research to clients and society. For dealing with this dilemma, it will be necessary to build a series of studies linking improvement in specific aspects of short-term outcome to longer term outcome and ultimately benefit to society.