Selecting relevant effects in factorial designs

Industrial contexts tend to be as much or more concerned about the probability of ignoring an effect when its influence on the response is relevant (Type II error) than about the probability of considering an effect to be active when in fact, it is not (Type I error). Here, we present a methodology for taking into account both types of error by fixing an effect value that is considered large enough to control the probability of it going unnoticed. In addition, we propose a plot to visualize the results obtained.