A Study of Factor Effects in Data From Factorial Experiments

This paper documents a meta-analysis of 113 data sets from published factorial experiments. The study quantifies regularities observed among factor effects and multi-factor interactions. Such regularities are known to be critical to efficient planning and analysis of experiments and to robust design of engineering systems. Three previously observed properties are analyzed — effect sparsity, hierarchy, and heredity. A new regularity is introduced and shown to be statistically significant. It is shown that a preponderance of active two-factor interaction effects are synergistic, meaning that when main effects are used to increase the system response, the interaction provides an additional increase. The potential implications for robust design are discussed.