From the point of view of your genes, likely to reproduce and beneficial are exactly the same thing. That’s trivially true.
Also not particularly interesting even if true: crazy beliefs that get you killed or prevent you from breeding have to spread non-parentally. They don’t have to be particularly persuasive or virulent, there just has to be some other mechanism (e.g. state control of education, military discipline, enjoyable but idiotic forms of mass entertainment) to spread them.
The prevalence of these means doesn’t even have to depend on the ones spreading the memes adopting them for themselves: there can be an economic motive for those who promote them for others to believe crazy things; like there is for drug dealers to sell rather than use heroin.
I’ve never been completely happy with the “I could make 1M similar statements and be wrong once” test. It seems, I dunno, kind of a frequentist way of thinking about the probability that I’m wrong. I can’t imagine making a million statements and have no way of knowing what it’s like to feel confidence about a statement to an accuracy of one part per million.
Other ways to think of tiny probabilities:
(1) If probability theory tells me there’s a 1 in a billion chance of X happening, then P(X) is somewhere between 1 in a billion and P(I calculated wrong), the latter being much higher.
If I were running on hardware that was better at arithmetic, P(I calculated wrong) could be got down way below 1 in a billion. After all, even today’s computers do billions of arithmetic operations per second. If they had anything like a one-in-a-billion failure rate per operation, we’d find them much less useful.
(2) Think of statements like P(7 is prime) = 1 as useful simplifications. If I am examining whether 7 is prime, I wouldn’t start with a prior of 1. But if I’m testing a hypothesis about something else and it depends on (among other things) whether 7 is prime, I wouldn’t assign P(7 is prime) some ridiculously specific just-under-1 probability; I’d call it 1 and simplify the causal network accordingly.