The benefits of understanding variation apply on multiple levels. Local policymakers and practitioners need to know both the average impact of an intervention and its variation across settings to properly assess its likely benefits and risks for their jurisdictions. For social scientists, cross-site impact variation offers opportunities to learn about mechanisms or mediators through which interventions produce their impacts and characteristics of settings and sample members that influence or moderate these impacts. And for researchers designing studies, cross-site impact variation can markedly affect the statistical precision of effect estimates and hence influence the sample size requirements for these estimates. One important lesson involves reporting cross-site impact variation and reflects the difference between variation in impact estimates and variation in true impacts. It is a simple matter to produce internally valid estimates of the mean impact of an intervention for each site in a multisite randomized trial. However, reporting cross-site variation in these estimates through a frequency distribution or a standard deviation can greatly overstate the amount of true variation that exists. This can occur because differences between site-specific impact estimates have two sources: (1) true cross-site impact differences and (2) differences in random, site-specific estimation error. For studies without very large site samples, most of the variation in site-specific impact estimates reflects random estimation error. Thus, it is essential to use a rigorous method for inferring the magnitude of true cross-site impact variation.