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I find myself firmly in Jeffreys’ camp these days. I have some data D. I have two hypotheses H0 and H1. If P(D|H1)>>P(D|H0), then I accept H1 and reject H0, unless there is some other evidence to consider. Fischer’s arguments about the probability of observations “at least as extreme” as a given observation make no sense to me any more. What if the density is on a circle? Then there is no tail. What if the density has zero value at the mean? Then events are *less* likely as they become less extreme.

However, I am not sure of the practical difference. For the normal distribution, you can come up with a Jeffreys-style hypothesis test based on P(D|H0)/P(D|H1) < .05. and get some critical value different from the classic 1.96. But since the .05 is completely arbitrary anyway, what difference does it really make?