I haven’t! Seems like a cool idea, though likely a good bit harder than p, df, and test stat correspondence.. but would be cool!

For Python: github.com/ACCLAB/dabestr

For R: github.com/ACCLAB/DABES...

Proud to have played a small part in this as a data-collection site. And so proud of @luis-a-gomez.bsky.social , who organized this project at our university as an undegrad and has launched to great success in a clinical psych PhD program at Purdue.

And, of course, each barrier raised to junk will have some false positives or harms, so the more content-based protections added, the marginally worse off the good-faith scientists.

Maybe we need some type of reputational scoring system for individuals, institutions, and journals.

Of course, journals are trying: www.science.org/content/arti...

But this is whack-a-mole - this policy will bar one specific type of junk, and only after hundreds of junk papers have been indexed into the literature. I don't think journals are equipped to respond quickly to this kind of threat.

Smallest of interest is a good approach. But I do think, in general, that hypothesis testing should be for testing quantitative hypotheses, and that there is no shame in the careful observational and exploratory work needed to derive clear quantitative predictions.

Biggest predictor of success was sample size, but largely due to ability to detect trivial effects. We need to realize that a non-quantitative hypothesis isn’t developed enough for hypothesis testing— if you can’t yet specify an expected effect size, keep working until you can, and *then* test.

Seems like some critical thinking about effect sizes is needed most of all: we shouldn’t still be pouring important resources into hypotheses that are just “more” or “different”. A researcher who doesn’t have a clear effect sizes expectation isn’t ready for a hypothesis test.