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Statistical Inference as Severe Testing
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Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long
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Hardcover, 486 pages
Published
November 22nd 2018
by Cambridge University Press
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I think I'd be wasting my time continuing with this. I'm not a statistician or a user of statistical methods, so I'm not really in a position to judge the book -- it's just not for me. A couple of thoughts anyway:
- The style is strange; it gave me the feeling of being dropped into the author's train of thought, or perhaps thrown headlong into its path. I don't think that was due to the complexity of the ideas, or even to the semantic density of the writing, but rather to some combination of stre ...more
- The style is strange; it gave me the feeling of being dropped into the author's train of thought, or perhaps thrown headlong into its path. I don't think that was due to the complexity of the ideas, or even to the semantic density of the writing, but rather to some combination of stre ...more
I skipped excursion 5 because I couldn't take any more normal hypothesis testing examples. I have a long list of questions to answer for myself after reading this, but they are mostly about basic tenets of various statistical philosophies rather than severity itself. My (very naive) impression is that the primary contribution of severity is as justification for frequentist inference without having to refer to long run performance. I remain unclear on how severity and post-hoc power analysis are
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“The influence of the biased selection is not on the believability of H but rather on the capability of the test to have unearthed errors. The error probing capability of the testing procedure is being diminished. If you engage in cherry picking, you are not “sincerely trying,” as Popper puts it, to find flaws with claims, but instead you are finding evidence in favor of a well-fitting hypothesis that you deliberately construct – barred only if your intuitions say it’s unbelievable. The job that was supposed to be accomplished by an account of statistics now has to be performed by you. Yet you are the one most likely to follow your preconceived opinions, biases, and pet theories.”
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“If you report results selectively, it becomes easy to prejudge hypotheses: yes, the data may accord amazingly well with a hypothesis H, but such a method is practically guaranteed to issue so good a fit even if H is false and not warranted by the evidence. If it is predetermined that a way will be found to either obtain or interpret data as evidence for H, then data are not being taken seriously in appraising H. H is essentially immune to having its flaws uncovered by the data. H might be said to have “passed” the test, but it is a test that lacks stringency or severity. Everyone understands that this is bad evidence, or no test at all. I call this the severity requirement.”
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