To be fair, in practice data could eventually change your mind. If you saw enough successes you’d conclude that the initial restriction was not appropriate. But the Bayesian formalism could accommodate unexpected data without having to change the model.

That seems more honest to me. Much more honest than the Bayesian fuge of saying p is uniform which leads to adding one success and one failure i.e. the estimate 11/12.

And frequentisits do NOT assert that probabilities are 0. They sometimes POINT estimate a probability to be zero, but always aim to accompany this with a confidence interval. Again, 10/10 successes does not lead to an assertion that p=1, it leads to a 95% interval (0.741,1).

I figured that out in the wee hours of the morning after I realized that what I wrote wasn’t even what I originally meant, which I’d for some reason calculated as 2% in my mind.

I’d thought at first that it must be “around 1:1000″ but then decided there must be a trick to it. In the clarity of the night I thought ” if I flip the coin two times, what are the possibilities of it coming up heads twice in a row — four combinations, 1/4, or 1/2 * 1/2.” It wasn’t hard to extropolate that out, since counting by exponents is a great way to waste time in school. And besides, anyone who doesn’t know 2^10 shouldn’t be using a computer.

What exactly are the odds of guessing the right coin-toss 10 times in a row. My non-statistical mind would guess 50% * 10 = 5%. That’s assuming the drunk didn’t by chance have a weighted coin, too.