Exploits the sparsity-of-effects principle that a system is usually dominated by main effects and low-order interactions, and thus only a few effects in a factorial experiment will be statistically significant. The subset is chosen to expose information about the most important features of the problem studied, using only a fraction of the experimental runs and resources of a full factorial design.
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