Some risks in the construction and analysis of supersaturated designs

A designed experiment in which the number of factors is at least as large as the number of runs is referred to as a supersaturated (SS) design. Recently these designs have received increased attention. Construction of such designs and analysis of data from them have been discussed by several authors. Our objective in this article is to examine these designs and methods for their analysis. An important finding for practitioners is that the correlation structure inherent in SS designs can obscure real effects or promote nonreal effects. Whatever analysis method is used, this problem can occur, although all-subsets regression is preferable to stepwise regression. Hence, one should be cautious with the use of SS designs.