3 ms·

Huh. Wouldn't publication bias dramatically impact a Bayesian's conclusions?

• ta988 3y ago
  Depends how you model it. I would say not taking into account the publication bias if you have ways to measure or at least estimate its distribution is a bigger mistake to make.
• ChadNauseam 3y ago
  Yes, it should affect anyone's conclusions unless they know exactly how much publication bias there is and then account for it
• pacbard 3y ago
  Yes, possibly. Publication bias is a known problem when using prior work (see [1] for some references on the problem and potential solutions). One of the strengths of Bayesian methods is that you can use a less informative prior to model your skepticism of prior work. [1]: https://www.sciencedirect.com/topics/medicine-and-dentistry/funnel-plot#:~:text=In%20a%20funnel%20plot%2C%20the,plots%20may%20suggest%20publication%20bias https://www.sciencedirect.com/topics/medicine-and-dentistry/....
• epgui 3y ago
  Yes, but no more than a non-Bayesian’s. The difference is mainly that the Bayesian approach makes more of its assumptions explicit.
  • jvans 3y ago
    This. Bayesian assumptions are explicit and you'll be forced to defend your priors. Frequentist assumptions like homoscedasticity are implicit and rarely challenged. I wouldn't be surprised if many people using frequentist methods have no idea what assumptions need to be true for the methods to be valid. In a Bayesian framework you can't avoid the assumptions
• ajkjk 3y ago
  That's why it's for constructing a prior that you change later; your study itself should update the prior in the direction of being more correct. But it's not like you can do better than "the best available prior" (by definition).