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Different critical values deduced by simulation have been proposed that greatly improve Lenth's original proposal. However, these simulations assume that all effects are zerosomething not realistic--producing bigger than desired critical values and thus significance levels lower that intended. This article, in accordance with George Box [2] well known idea that Experimental Design should be about learning and not about testing and based on studying how the presence of a realistic number and size of active effects affects critical values, proposes to use t = 2 for any number of runs equal or greater than 8. And it shows that this solution, in addition of being simpler, provides under reasonable realistic situations better results than those obtained by simulation.

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When analysing the effects of a factorial design, it is customary to take into account the probability of making a Type I error (the probability of considering an effect significant when it is non-significant), but not to consider the probability of making a Type II error (the probability of considering an effect as non-significant when it is significant). Making a Type II error, however, may lead to incorrect decisions regarding the values that the factors should take or how subsequent experiments should be conducted. In this paper, we introduce the concept of minimum effect size of interest and present a visualization method for selecting the critical value of the effects, the threshold value above which an effect should be considered significant, which takes into account the probability of Type I and Type II errors. Copyright