My previous article examined how an equivalence test can shift the burden of proof when you perform a hypothesis test of the means. This allows you to more rigorously test whether the process mean is equivalent to a target or to another mean.

Here’s another key difference: To perform the analysis, an equivalence test requires that you first define, upfront, the size of a practically important difference between the mean and the target, or between two means.

Truth be told, even when performing a standard hypothesis test, you should know the value of this difference. Because you can’t really evaluate whether your analysis will have adequate power without knowing it. Neither can you evaluate whether a statistically significant difference in your test results has significant meaning in the real world, outside of probability distribution theory.

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