Doesn't this only work if the logic of the situation is transparent? Or maybe I'm misunderstanding what you mean by the logic of the situation. Are you trying to say "Keep in mind the counterfactual situations in which you might make various decisions and then determine which situation you are in?"
Or maybe "determine your policy in all statistically likely scenarios then determine which scenario you are in"
On your first point. If better is defined as affect on crime, dependency, poverty, and mental-illness I would expect NO to "negligible" difference between the two. It's a minor disagreement I guess.
On your second point. I feel like the answer to this question is subjective and depends largely on how much someone values the future. I'm pretty optimistic about it so I think it's worth the 0.05% chance it would give me as opposed.
There isn't much of a difference between Frequentist statistics and Bayesian statistics.
Statistics Question here.
In a binomial distribution why is it useful to check if N * phat > 5 AND N * (1 - phat) > 5 when determining if it is a normal distribution?
where:
N = number of samples
phat = number of successes / N
I've never been a big user of Wikipedia.
If I believed this to be true I think I would take your position. But because you would not change your mind if you believed this was false I too, do not believe this counts as the crux of our disagreement.
I'll give it a shot this time. My proposed crux is that much of what we believe about the causes of poverty (crime... ect. ) are likely false in such a way that we are completely missing something conceptual in our models (including the one you stated above) or the causes are more powerful than our greatest operational intitutions can influence. (Age, genetics, ect)