Type I errors are a feature of frequentist statistics. If you don't use null hypotheses you don't make type I errors. Bayesians don't make type I errors because they don't have null hypotheses.
LOL. That's, um, not exactly true.
Let's take a new drug trial. You want to find out whether the drug has certain (specific, detectable) effects. Could you please explain how a Bayesian approach to the results of the trial would make it impossible to make a Type I error, that is, a false positive: decide that the drug does have effects while in fact it does not?
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.
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)