Oops, missed that; but that specification doesn’t hold in the situation we care about, since rejecting the null hypotheses typically requires us to consider the result of marginalizing over a space of alternative hypotheses (well, assuming we’re being Bayesians, but I know you prefer that anyways =P).
Well, right, assuming we’re Bayesians, but when we’re just “rejecting the null hypothesis” we should mostly be concerned about likelihood from the null hypothesis which has no moving parts, which is why I used the log approximation I did. But at this point we’re mixing frequentism and Bayes to the point where I shan’t defend the point further—it’s certainly true that once a Bayesian considers more than exactly two atomic hypotheses, the update on two independent pieces of evidence doesn’t go as the square of one update (even though the likelihood ratios still go as the square, etc.).
That’s why I specified single possible worlds / hypotheses with no internal parameters that are being learned.
Oops, missed that; but that specification doesn’t hold in the situation we care about, since rejecting the null hypotheses typically requires us to consider the result of marginalizing over a space of alternative hypotheses (well, assuming we’re being Bayesians, but I know you prefer that anyways =P).
Well, right, assuming we’re Bayesians, but when we’re just “rejecting the null hypothesis” we should mostly be concerned about likelihood from the null hypothesis which has no moving parts, which is why I used the log approximation I did. But at this point we’re mixing frequentism and Bayes to the point where I shan’t defend the point further—it’s certainly true that once a Bayesian considers more than exactly two atomic hypotheses, the update on two independent pieces of evidence doesn’t go as the square of one update (even though the likelihood ratios still go as the square, etc.).