  An important issue for many economic experiments is how the experimenter can
ensure sufficient power for rejecting one or more hypotheses. Here, we apply
methods developed mainly within the area of clinical trials for testing
multiple hypotheses simultaneously in adaptive, two-stage designs. Our main
goal is to illustrate how this approach can be used to improve the power of
economic experiments. Having briefly introduced the relevant theory, we perform
a simulation study supported by the open source R package asd in order to
evaluate the power of some different designs. The simulations show that the
power to reject at least one hypothesis can be improved while still ensuring
strong control of the overall Type I error probability, and without increasing
the total sample size and thus the costs of the study. The derived designs are
further illustrated by applying them to two different real-world data sets from
experimental economics.
