Outperforming completely randomized designs.

Abstract

Bi-randomization designs have become increasingly popular in industry because some of the factors under investigation are often hard-to-change. It is well-known that the resulting compound symmetric error structure not only affects estimation and inference procedures but also the efficiency of the experimental designs used. In this paper, the use of bi-randomization designs is shown to outperform completely randomized designs in terms of D-efficiency. This result suggests that bi-randomization designs should be considered as an alternative to completely randomized designs even if all experimental factors are easy-to-change.Optimal;