What is the impact of sample selection on the inference payoff of an evaluator testing a simple hypothesis based on the outcome of a location experiment? We show that anticipated selection locally reduces noise dispersion and thus increases informativeness if and only if the noise distribution is double logconvex, as with normal noise. The results are applied to the analysis of strategic sample selection by a biased researcher and extended to the case of uncertain and unanticipated selection. Our theoretical analysis offers applied research a new angle on the problem of selection in empirical studies, by characterizing when selective assignment based on untreated outcomes benefits or hurts the evaluator.