Experiments that fail to replicate at percentages greater than those expected from published confidence values (say, posterior probabilities) are evidence that the published confidence values are wrong.
How would you know? People do not produce posterior probabilities or credible intervals, they produce confidence intervals and p-values.
I don't see how this point helps you.
Either the p-values in the papers are worthless in the sense of not reflecting the probability that the observed effect is real -- in which case the issue in the parent post stands.
Or the p-values, while not perfect, do reflect the probability the effect is real -- in which case they are falsified by the replication rates and in which case the issue in the parent post stands.
For those who haven't heard, NIH and NSF are no longer processing grants, leading to many negative downstream effects.
I've been directing my attention elsewhere lately and don't have anything informative to say about this. However, my uninformed intuition is that people who care about effective altruism (research in general, infrastructure development, X-risk mitigation, life-extension...basically everything, actually) or have transhumanist leanings should be very concerned.
The consequences have already been pretty disastrous. To provide just one, immediate example, the article says that the Center for Disease Control and Prevention has shut down. I think that this is almost certain to directly cause a nontrivial number of deaths. Each additional day that this continues could have huge negative impact down the line, perhaps delaying some key future discoveries by years. This event *might* be a small window of opportunity to prevent a lot of harm very cheaply.
So the question is:
1) Can we do anything to remedy the situation?
2) If so, is it worth doing it? (Opportunity costs, etc)