Increasingly, researchers are interested in questions regarding treatment-effect variation across partially or fully latent subgroups defined not by pretreatment characteristics but by postrandomization actions. One promising approach to address such questions is principal stratification. Under this framework, a researcher defines endogenous subgroups, or principal strata, based on post-randomization behaviors under both the observed and the counterfactual experimental conditions. These principal strata give structure to such research questions and provide a framework for determining estimation strategies to obtain desired effect estimates. This article provides a nontechnical primer to principal stratification. We review selected applications to highlight the breadth of substantive questions and methodological issues that this method can inform. We then discuss its relationship to instrumental variables analysis to address binary noncompliance in an experimental context and highlight how the framework can be generalized to handle more complex posttreatment patterns. We emphasize the counterfactual logic fundamental to principal stratification and the key assumptions that render analytic challenges more tractable. We briefly discuss technical aspects of estimation procedures, providing a short guide for interested readers.