Abstract
There are many situations in which parametric statistical techniques are less than ideal for evaluating a simulated system. For most simulation output, one must rely heavily on the central limit theorein in order to apply parametric statistical techniques. The bootstrap statistic is a nonparametric sample-resample technique that makes no distributional assumptions and may be used for estimation and hypothesis testing. The authors propose the bootstrap as a valuable tool for the analysis of simulation output data since it can be used in situations in which either the distribution is not known or normal approximations are inappropriate. Furthermore, since bootstrapping is itself a simulation technique it is inherently satisfying as a tool for the analysis of simulation output data. Illustrations are presented.
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