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Chapter 9. Six Enduring Controversies in... > What If You Need to Run More Than On...

What If You Need to Run More Than One Test?

“In 1972 Maurice Kendall commented on how regrettable it was that during the 1940s mathematics had begun to ‘spoil’ statistics. Nowhere is this shift in emphasis from practice, with its room for intuition and pragmatism, to theory and abstraction, more evident than in the area of multiple comparison procedures. The rules for making such comparisons have been discussed ad nauseam and they continue to be discussed” (Cowles, 1989, p. 171).

On One Hand

When the null hypothesis of no difference is true, you can think of a single test with α = 0.05 as the flip of a single coin that has a 95% chance of heads (correctly failing to reject the null hypothesis) and a 5% chance of tails (falsely concluding there is a difference when there really isn’t one—a false alarm, a Type I error). These are the probabilities for a single toss of the coin (a single test), but what if you run more than one test? Statisticians sometimes make a distinction between the error rate per comparison (EC) and the error rate per family (EF, or family-wise error rate) (Myers, 1979).


  

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