Saying "no" to everything is no different than saying "yes" to everything from a logical perspective.
This is incorrect. The base rate of true findings when talking about causal models is extremely low. If you say "no" to every published finding, you will be right much more often than you're wrong.
Now, that doesn't mean you should say no to every published finding. But the idea that "yes" and "no" should be equally weighted in your priors across the board is an inaccurate representation of the state of research, and the underlying facts of the universe.
The actual prior distribution of effective to ineffective models is extremely hard to infer from everyday life. We don't have access to unbiased data sources. That's why we should focus on inquiry rather than canned responses.
As an example of how this can be misleading common the average person sees only a tiny fraction of the proposed models that are much more likely to be valid because they've passed far enough along the process of publishing to have received some scrutiny and some credibility in the average case.
However, I would say that if you don't have the time/desire to investigate, your comments in public forums probably aren't worth listening to. So, I do agree that people that simply respond with "correlation != causality" without reading the study probably shouldn't be doing that.
> As an example of how this can be misleading common the average person sees only a tiny fraction of the proposed models that are much more likely to be valid because they've passed far enough along the process of publishing to have received some scrutiny and some credibility in the average case.
Ya, that's definitely true. The base rate of true models in published research is almost tautologically higher than the base rate of true models amongst all possible models. I was going to say that it is tautological...but I suppose it's actually not. All published models could be false. But I certainly agree that the research process is a pretty good filter, and the base rate of true models is almost certainly higher. But i'd be very very surprised if it was higher than 50%, even if you use a fairly high standard for "published research".
The difference is that in math "here is a proof" is all you need, while in sciences people are typically sharing what amounts to collections of observations. If you publish the collections that lead to interesting conclusions, but not the ones that are boring, people looking at what's been published are misled.