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An important issue for many economic experiments is how the experimenter canensure sufficient power for rejecting one or more hypotheses. Here, we applymethods developed mainly within the area of clinical trials for testingmultiple hypotheses simultaneously in adaptive, two-stage designs. Our maingoal is to illustrate how this approach can be used to improve the power ofeconomic experiments. Having briefly introduced the relevant theory, we performa simulation study supported by the open source R package asd in order toevaluate the power of some different designs. The simulations show that thepower to reject at least one hypothesis can be improved while still ensuringstrong control of the overall Type I error probability, and without increasingthe total sample size and thus the costs of the study. The derived designs arefurther illustrated by applying them to two different real-world data sets fromexperimental economics.

Improving the Power of Economic Experiments Using Adaptive Designs