Must a process be in statistical control before conducting designed experiments?

Fisher demonstrated three quarters of a century ago that the three key concepts of randomization, blocking, and replication make it possible to conduct experiments on processes that are not necessarily in a state of statistical control. However, even today there persists confusion about whether statistical control is a necessary prerequisite for conducting valid experiments in industry. In this article we revisit and extend Fisher's original argument. Reusing his 1925 examples, we demonstrate that the need for statistical control as a prerequisite for conducting industrial experiments is misconceived. Clarifying this issue may help quality practitioners identify new and wider opportunities for the use of designed experiments in industrial practice.