Outperforming Completely Randomized Designs

Split-plot designs have become increasingly popular in industrial experimentation 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, we compute D-optimal first and second order split-plot designs and show that these designs, in many cases, outperform completely randomized designs in terms of D- and G-efficiency. This suggests that split-plot designs should be considered as an alternative to completely randomized designs even if running a completely randomized design is affordable.