Testing Hypotheses on Simulated Data: Why Traditional Hypotheses-Testing Statistics Are Not Always Adequate for Simulated Data, and How to Modify Them
Richard Aló*, Vladik Kreinovich**, and Scott A. Starks**
*Center for Computational Sciences and Advanced Distributed Simulation, University of Houston-Downtown, One Main Street, Houston, TX 77002, USA
**Pan-American Center for Earth and Environmental Studies, University of Texas at El Paso, El Paso, TX 79968, USA
To check whether a new algorithm is better, researchers use traditional statistical techniques for hypotheses testing. In particular, when the results are inconclusive, they run more and more simulations (n2>n1, n3>n2, …, nm>nm-1) until the results become conclusive. In this paper, we point out that these results may be misleading. Indeed, in the traditional approach, we select a statistic and then choose a threshold for which the probability of this statistic “accidentally” exceeding this threshold is smaller than, say, 1%. It is very easy to run additional simulations with ever-larger n. The probability of error is still 1% for each ni, but the probability that we reach an erroneous conclusion for at least one of the values ni increases as m increases. In this paper, we design new statistical techniques oriented towards experiments on simulated data, techniques that would guarantee that the error stays under, say, 1% no matter how many experiments we run.
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