These days, so many theater productions are updated reviews of older standards. Same with the comedy hours at the Bayesian retreat, and task force meetings of significance test reformers. So (on the 1-year anniversary of this blog) let’s listen in to one of the earliest routines (with highest blog hits), but with some new reflections (first considered here and here).
‘ “Did you hear the one about the frequentist . . .
“who claimed that observing “heads” on a biased coin that lands heads with probability .05 is evidence of a statistically significant improvement over the standard treatment of diabetes, on the grounds that such an event occurs with low probability (.05)?”
The joke came from J. Kadane’s Principles of Uncertainty (2011, CRC Press*).
“Flip a biased coin that comes up heads with probability 0.95, and tails with probability 0.05. If the coin comes up tails reject the null hypothesis. Since the probability of rejecting the null hypothesis if it is true is 0.05, this is a valid 5% level test. It is also very robust against data errors; indeed it does not depend on the data at all. It is also nonsense, of course, but nonsense allowed by the rules of significance testing.” (439)
Much laughter.
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But is it allowed? I say no. The null hypothesis in the joke can be in any field, perhaps it concerns mean transmission of Scrapie in mice (as in my early Kuru post). I know some people view significance tests as merely rules that rarely reject erroneously, but I claim this is mistaken. Both in significance tests and in scientific hypothesis testing more generally, data indicate inconsistency with H only by being counter to what would be expected under the assumption that H is correct (as regards a given aspect observed). Were someone to tell Prusiner that the testing methods he follows actually allow any old “improbable” event (a stock split in Apple?) to reject a hypothesis about prion transmission rates, Prusiner would say that person didn’t understand the requirements of hypothesis testing in science. Since the criticism would hold no water in the analogous case of Prusiner’s test, it must equally miss its mark in the case of significance tests**. That, recall, was Rule #1. Continue reading
Error Statistics Philosophy 