Will_Newsome comments on How to Fix Science - Less Wrong
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Bayesian methods are better in a number of ways, but ignorant people using a better tool won't necessarily get better results. I don't think the net effect of a mass switch to Bayesian methods would be negative, but I do think it'd be very small unless it involved raising the general statistical competence of scientists.
Even when Bayesian methods get so commonplace that they could be used just by pushing a button in SPSS, researchers will still have many tricks at their disposal to skew their conclusions. Not bothering to publish contrary data, only publishing subgroup analyses that show a desired result, ruling out inconvenient data points as "outliers", wilful misinterpretation of past work, failing to correct for doing multiple statistical tests (and this can be an issue with Bayesian t-tests, like those in the Wagenmakers et al. reanalysis lukeprog linked above), and so on.
ISTM a large benefit of commonplace Bayes would be that competent statisticians could do actually meaningful meta-analyses...? Which would counteract widespread statistical ineptitude to a significant extent...?
I'm not sure it'd make much difference. From reading & skimming meta-analyses myself I've inferred that the main speedbumps with doing them are problems with raw data themselves or a lack of access to raw data. Whether the data were originally summarized using NHST/frequentist methods or Bayesian methods makes a lot less difference.
Edit to add: when I say "problems with raw data themselves" I don't necessarily mean erroneous data; a problem can be as mundane as the sample/dataset not meeting the meta-analyst's requirements (e.g. if the sample were unrepresentative, or the dataset didn't contain a set of additional moderator variables).