[mikeiz404]

I believe you are saying that knowing (or believing) the probability of a group of papers being fraudulent does not help you (ex: in a high fraud belief of say 45%) make a better decision because the group probability does not inform any single paper probability meaningfully since a paper's findings are close to being all or none. Is that correct?

If so, I see your point.

I still think in a more diverse set of scenarios where an intervention has more than a single and binary outcome, knowing the group probability can still be informative. For example if an intervention in a possibly fraudulent study shows a large upside but other known to be unfraudulent studies show a severe but unlikely downside then it may not be a good idea to do the intervention unless there is a good reason to (ex: all known safer interventions have been tried, known risk can be mitigated, end of life and patient agrees, …).

But I am beginning to wonder how useful of a signal a known fraud percent would be given how long it takes for fraud to be discovered and then disclosed.

I still think something can be done here with public statistics and perhaps reputation but I’ll have to think about it some more. Certainly if an author or institution were more at risk for discovered fraud or fraud discovery was more likely they would do a better job of policing or not doing it. Other incentives (as mentioned in the article) are at play as well.