Abstract
We present a two-stage experiment design for use in simulation experiments that compare systems in terms of their expected (long-run average) performance. This procedure simultaneously achieves the following with a prespecified probability of being correct: (a) find the best system or a near best system; (b) identify a subset of systems that are more than a practically insignificant difference from the best; and (c) provide a lower bound on the probability that the best or near best system has actually been selected. The procedure assumes normally distributed data, but allows unequal variances.
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