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Determining the Statistical Power of the Kolmogorov-Smirnov and Anderson-Darling Goodness-of-Fit Tests via Monte Carlo Simulation

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Center of Naval Analyses Arlington United States

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Metrics are often used to compare the performance of newly developed systems with the performance of their predecessors. Metrics can also be used to compare the output of a simulator with real-world data to test the accuracy of the simulation.Statistical comparison of these metrics can be necessary when making such a determination. There are different methods of statistical comparison that are sensitive to the various types of underlying distribution of the metric data. Distribution type can affect the performance of these tests, and, fortunately, the distributions of many common metrics are well known. For example, mean time to repair MTTR and mean flight hours between critical failures MFHBCF, generally follow a log-normal and an exponential distribution, respectively. This paper presents the effects of distribution type and parameters on the statistical power of two common goodness-of-fit tests KolmogorovSmirnov and Anderson-Darling via Monte Carlo simulation.

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Approved For Public Release;

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