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Regardless of the pros and cons of Bayesian methods, here is what I believe is needed: - Pre-register all studies, declaring sample sizes and power analysis.
by rwilson4 4y ago
Regardless of the pros and cons of Bayesian methods, here is what I believe is needed:
- Pre-register all studies, declaring sample sizes and power analysis.
- Report results regardless of outcome. Eliminate the "we only publish stat sig results" baloney.
- Report confidence/credible intervals, adjusting for multiple comparisons as appropriate. Plot the posterior distribution of the effect size if appropriate.
- Publish all data and code.
- Provide funding for duplicating important studies.
- dalbasal 4y agoAutor doesn't appear to be talking about research/publishing. He just wants frequentism banned for undergrads.
- uniqueuid 4y ago100% this. It's not even hard today, just a cultural shift. I'd add one additional technique: Specification curve analyses. Bonferroni etc. alone won't help against systematic bias in a field and/or misconduct, and specification curves are easy to do, e.g. with specR [1]. [1] https://github.com/masurp/specr https://github.com/masurp/specr
- ngcc_hk 4y agoCan freq. do that and improve as well?
- uniqueuid 4y agoSure. The only thing that's hard is to get a full posterior distribution and to add priors. But the rest can be done in frequentist stats as well. [edit] you can get posteriors with bootstrapping etc. - it's not precisely the same but better than nothing.
- skybrian 4y agoI don't know how often this is practical, but here's an alternative view: preregistration is unimportant when you are looking for enormous effect sizes, and you should do that if you can: https://slimemoldtimemold.com/2022/07/21/on-the-hunt-for-ginormous-effect-sizes/ https://slimemoldtimemold.com/2022/07/21/on-the-hunt-for-gin...
- deleted 4y ago[deleted]