Fractional factorial designs that maximise the probability of identifying the important factors

Abstract

We use simulation to evaluate the abilities of fractional factorial designs and associated analysis methods to achieve model identification-related objectives. We show that these simulations can provide potentially useful insights to decision makers before experimentation begins. Findings include that Type II error rates might be higher than is commonly realised. Also, we propose new balanced and unbalanced fractional factorial designs, derived from simulation optimisation, that maximise the probability of correct selection of which factors are important and which are unimportant.