> The prediction could still be correct even when the actual event is not what was predicted. This is true (and conversely, a prediction can still be "wrong" e

> The prediction could still be correct even when the actual event is not what was predicted. This is true (and conversely, a prediction can still be "wrong" even if the outcome matches the prediction, if the prediction expresses the wrong degree of confidence). However: > when statistics can only tell us something about it when repeating it This is only true if you take a pure frequentist approach to probability. The Bayesian interpretation of probability has no problem with defining probabilities over single-occurrence events. (And even frequentists have a way of dealing with this, although Bayesians would argue that in doing so they're essentially adopting a form of Bayesian probability).