[HN Gopher] Not Frequentist Enough
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Not Frequentist Enough
Author : Tomte
Score : 16 points
Date : 2022-12-02 15:03 UTC (7 hours ago)
(HTM) web link (statmodeling.stat.columbia.edu)
(TXT) w3m dump (statmodeling.stat.columbia.edu)
| boole1854 wrote:
| Wow, I could have _sworn_ I read this a while back, but the post
| date is today! What?
|
| For anyone else that this occurs to, here's the explanation: a
| similar post with only a few wording differences was posted back
| in October:
| https://statmodeling.stat.columbia.edu/2022/10/05/not-freque...
| squaredot wrote:
| I came to the comment section to look for this comment! Thanks.
|
| Maybe the reposting is part of a frequentist study. I'll call
| it article laundering.
| yamrzou wrote:
| Yes: https://news.ycombinator.com/item?id=33114403
| topaz0 wrote:
| I had exactly the same reaction. I wonder what happened. Thanks
| for sleuthing.
| blamestross wrote:
| The root challenge of all modern frequentist statistics is "does
| the selection criteria for the sample actually allow me to
| generalize the results". Experiments and post-hoc analysis are
| really sensitive to selection biases and researchers (and much
| worse the media) tend to overgeneralize the results of a study.
| Its particularly bad in medicine. My personal bugbear is that you
| can only participate in a study for an autoimmune disorder if you
| only have the 1 disorder of focus, but real people with
| autoimmune disorders tend to have more than one (diagnosed or
| not). So studies on treatment efficacy basically can't actually
| generalize to the populations they intend to treat because they
| select against them.
| kkoncevicius wrote:
| > The root challenge of all modern frequentist statistics is
| "does the selection criteria for the sample actually allow me
| to generalize the results".
|
| Why did you single out frequentists here? This is a challenge
| for all inference, prediction, classification, and any other
| "forecasting".
| blamestross wrote:
| All those things are either frequentist or baysian
| statistical models. Baysian models are much harder (often
| impossible) to use but at least pay attention to
| intersectionality.
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