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It sounds like you read Andrew Gelman's blog. You might be interested to review this post: https://statmodeling.stat.columbia.edu/2012/11/10/16808/ https://stat

It sounds like you read Andrew Gelman's blog. You might be interested to review this post: https://statmodeling.stat.columbia.edu/2012/11/10/16808/ https://statmodeling.stat.columbia.edu/2012/11/10/16808/ Frequentist statistics does not force you to accept any hypothesis test with a result p < 0.05 as definitive proof of something. It does not forbid considering prior probability of a result. It just doesn't formalize the consideration of prior probabilities because it is hard to distill this consideration into a formal recipe. Everyone agrees that Bayes' Rule is valid and important, the question is when and how best to use it. > If you use bayesian methods, you're pretty much forced to spend more time considering the effect size and credibility of your results, and you're basically required to report them. You're not forced to do those things well. Any scientific method can be cargo culted.

uniqueuid 4y ago
Yes I think Gelman is doing great work, as is e.g. McElreath [1]. The problem that I care about is not whether frequentist statistics can be taught and used well. They can, and I try in my teaching to do so. The problem is that empirically, frequentist statistics is a fig leaf for a ton of extremely problematic work. And pushing bayesian thinking is currently our best chance to fix this, because it's easier to do a shift in the mental framework than to fix the perception of an existing framework. [1] https://xcelab.net/rm/statistical-rethinking/ https://xcelab.net/rm/statistical-rethinking/
- civilized 4y ago
  I agree that more Bayesian thinking is needed. But I suspect that pushing the technicalities of Bayesian analysis (MCMC etc) would perversely lead to even more cargo-culting, as people would struggle with the technicalities and look desperately for quick fixes. Bayesian methodology benefits from having relatively much more statistically sophisticated practitioners, which leads to an optimism bias when we imagine how it would scale up.
  - uniqueuid 4y ago
    Perhaps you don't see it this way, but I take our eventual convergence as a sign that we don't actually disagree. It's rather that the problem we're starting from is different. Thanks for taking your time for this debate!
    - civilized 4y ago
      Likewise! This is a tricky problem and requires a lot of patient thought.

uniqueuid 4y ago

Yes I think Gelman is doing great work, as is e.g. McElreath [1]. The problem that I care about is not whether frequentist statistics can be taught and used well. They can, and I try in my teaching to do so. The problem is that empirically, frequentist statistics is a fig leaf for a ton of extremely problematic work. And pushing bayesian thinking is currently our best chance to fix this, because it's easier to do a shift in the mental framework than to fix the perception of an existing framework. [1] https://xcelab.net/rm/statistical-rethinking/ https://xcelab.net/rm/statistical-rethinking/

civilized 4y ago
I agree that more Bayesian thinking is needed. But I suspect that pushing the technicalities of Bayesian analysis (MCMC etc) would perversely lead to even more cargo-culting, as people would struggle with the technicalities and look desperately for quick fixes. Bayesian methodology benefits from having relatively much more statistically sophisticated practitioners, which leads to an optimism bias when we imagine how it would scale up.
- uniqueuid 4y ago
  Perhaps you don't see it this way, but I take our eventual convergence as a sign that we don't actually disagree. It's rather that the problem we're starting from is different. Thanks for taking your time for this debate!
  - civilized 4y ago
    Likewise! This is a tricky problem and requires a lot of patient thought.