Clearly you are very right in underlying one of many misinterpretations of the p statistics.

Yet, I feel you are too hard on Oreskes: she has a point in insisting on another fact: that the choice of .05 as default threshold in null hypothesis statistical significance testing makes no sense.

P<.05 as an accept/reject threshold is based on laziness, and misunderstanding of statistical reasoning. The threshold for P should be chosen -through decision theory- to minimise costs or maximise benefits, it can't be that in oncology (when people's life is in danger) one uses the same threshold as in "not life-or-death" decisions. Anyway, despite not being a physicist or a mathematician, or a statistician, I feel p values in NHSST are almost "rubbish" as they are too often misunderstood (as you said) and are very counter-intuitive ]]>

So you are right that p-values are perfectly valid. But they are also useful and interesting, in the right circumstances. And you are right as well that people who do not feel comfortable with statistics like this shouldn’t be publishing journals at all. The editors are right that these statistics are too often used to dress up uninteresting hypotheses, but they should be editing them out, not issuing fatwas. It is not an issue of mathematical validation, but of scientific relevance evaluation. Perhaps a more useful directive might be that all tests of this sort need to be throughly summarized / explained in words as well as in the p-value symbology.

Thanks for this post-

]]>all published experimental papers should require a sign-off by an actual phd in MATHEMATICAL STATISTICS (from a math department), and I do not

mean a biostats phd–I mean a mathematician.

(This reminds me of the story of the bad statistician who was caught trying to smuggle a bomb onto a plane. His defense was that he was trying to prevent terrorism: since the priority of a bomb on a plane is actually very low, the probability of *two* bombs on a plane must be negligible, and he wasn’t going to explode his.)

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