[rwilson4]

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.

[uniqueuid]

100% 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

[dalbasal]

Autor doesn't appear to be talking about research/publishing. He just wants frequentism banned for undergrads.

[skybrian]

I 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-gin...

[ngcc_hk]

Can freq. do that and improve as well?

[uniqueuid]

Sure. 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.