Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Abstract An important issue for many economic experiments is how the experimenter can ensure sufficient power in order to reject one or more hypotheses. The paper illustrates how methods for testing multiple hypotheses simultaneously in adaptive, two-stage designs can be used to improve the power of economic experiments. We provide a concise overview of the relevant theory and illustrate the method in three different applications. These include a simulation study of a hypothetical experimental design, as well as illustrations using two data sets from previous experiments. The simulation results highlight the potential for sample size reductions, maintaining the power to reject at least one hypothesis while ensuring strong control of the overall Type I error probability.