This article presents a conceptual clarification of asymmetric hypotheses and a discussion of methodologies available to test them. Despite the existence of a litany of theories that posit asymmetric hypotheses, most empirical studies fail to capture their core insight: boundaries separating zones of data from areas that lack data are substantively interesting. We discuss existing set-theoretic and large-N approaches to the study of asymmetric hypotheses, introduce new ones from the literatures on stochastic frontier and data envelopment analysis, evaluate their relative merits, and give three examples of how asymmetric hypotheses can be studied with this suite of tools.