Although a large-scale experiment can provide an estimate of the average causal impact for a program, the sample of sites included in the experiment is often not drawn randomly from the inference population of interest. In this article, we provide a generalizability index that can be used to assess the degree of similarity between the sample of units in an experiment and one or more inference populations on a set of selected covariates. The index takes values between 0 and 1 and indicates both when a sample is like a miniature of the population and how well reweighting methods may perform when differences exist. Results of simulation studies are provided that develop rules of thumb for interpretation as well as an example.