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Stats Science

Why P-values Cannot Tell You If a Hypothesis Is Correct 124

Posted by Soulskill
from the i'm-a-doctor-not-an-oracle dept.
ananyo writes "P values, the 'gold standard' of statistical validity, are not as reliable as many scientists assume. Critically, they cannot tell you the odds that a hypothesis is correct. A feature in Nature looks at why, if a result looks too good to be true, it probably is, despite an impressive-seeming P value."
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Why P-values Cannot Tell You If a Hypothesis Is Correct

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  • by sideslash (1865434) on Wednesday February 12, 2014 @05:59PM (#46232797)
    The world is full of coincidental correlations waiting to be rationalized into causality relationships.
  • Gold Standard? (Score:5, Interesting)

    by radtea (464814) on Wednesday February 12, 2014 @06:02PM (#46232819)

    That means "outmoded and archaic", right?

    I realize I have a p-value in my .sig line and have for a decade, but p-values were a mediocre way to communicate the plausibility of a claim even in 2003. They are still used simply because the scientific community--and even moreso the research communities in some areas of the social sciences--are incredibly conservative and unwilling to update their standards of practice long after the rest of the world has passed them by.

    Everyone who cares about epistemology has known for decades that p-values are a lousy way to communicate (im)plausibility. This is part and parcel of the Bayesian revolution. It's good that Nature is finally noticing, but it's not as if papers haven't been published in ApJ and similar journals since the '90's with curves showing the plausibility of hypotheses as positive statements.

    A p-value is the probability of the data occurring given the null hypothesis is true, and which in the strictest sense says nothing about the hypothesis under test, only the null. This is why the value cited in my .sig line is relevant: people who are innocent are not guilty. This rare case where there is an interesting binary opposition between competing hypothesis is the only one where p-values are modestly useful.

    In the general case there are multiple competing hypotheses, and Bayesian analysis is well-suited to updating their plausiblities given some new evidence (I'm personally in favour of biased priors as well.) The results of such an analysis is the plausibility of each hypothesis given everything we know, which is the most anyone can ever reasonably hope for in our quest to know the world.

    [Note on language: I distinguish between "plausibility"--which is the degree of belief we have in something--and "probability"--which I'm comfortable taking on a more-or-less frequentist basis. Many Bayesians use "probability" for both of these related by distinct concepts, which I believe is a source of a great deal of confusion, particularly around the question of subjectivity. Plausibilities are subjective, probabilities are objective.]

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