Let's take a new drug trial. You want to find out whether the drug has certain (specific, detectable) effects.
I don't. A real bayesian doesn't. The bayesian wants to know the probability which with the drug will improve the well being of a patient.
The output of a bayesian analysis isn't a truth value but a probability.
The output of a bayesian analysis isn't a truth value but a probability.
So is the output of a frequentist analysis.
However real life is full of step functions which translate probabilities into binary decisions. The FDA needs to either approve the drug or not approve the drug.
Saying "I will never make a Type I error because I will never make a hard decision" doesn't look good as evidence for the superiority of Bayes...
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)