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I think this is getting to the root of the problem — you're taking a perfectly valid frequentist view of probability, that is, viewing it as a series of discret

I think this is getting to the root of the problem — you're taking a perfectly valid frequentist view of probability, that is, viewing it as a series of discrete experiments. But the probabilities that were assigned to Clinton's victory were derived from Bayesian probability theory that estimates the likelihood that event occurs based on empirically determined prior probabilities that contribute to that event. Those prior distributions have uncertainty which leads to uncertainty in the resulting prediction. I apologize if I've explained this poorly, this is just my layman's understanding of the matter.

marcosdumay 8y ago
It's a property of the thing being measured, not of the measuring system. A Bayesian calculating that probability must get a single number too, without error margins. The only difference is that the Bayesian will have to weight his probabilities by how much of the interval is at the "win" and the "no win" scenarios. It is different if you are measuring how many votes each candidate will have. For that both methods must get intervals and a confidence level.