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> Probabilities without confidence intervals are by-and-large meaningless. That's just not true. If I believe that my team has a 20% chance to win and you offe

> Probabilities without confidence intervals are by-and-large meaningless. That's just not true. If I believe that my team has a 20% chance to win and you offer me a bet with anything better than 5-to-1 odds I should take the bet. If you offer me anything worse than 5-to-1, then I should not take the bet. There's no fuzz factor necessary; no confidence interval that I need to use to make the decision. Perhaps you're getting at the idea of calibration? That it's difficult for a person to know what a 20% chance feels like? But there are still a lot of situations where it's not up to human judgement.

williamscales 8y ago
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.
IanCal 8y ago
Perhaps a good way of putting it would be that there's probably not an extremely solid line like that 20%. You would leap at a bet with 10000-1 odds, and never go for 2-1. Let's say you can make these bets repeatedly. Would you always bet on 5.01-1 odds and never on 4.99-1 odds? Is it that odd to think that your team has about a 20% chance to win?

williamscales 8y ago

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.

IanCal 8y ago

Perhaps a good way of putting it would be that there's probably not an extremely solid line like that 20%. You would leap at a bet with 10000-1 odds, and never go for 2-1. Let's say you can make these bets repeatedly. Would you always bet on 5.01-1 odds and never on 4.99-1 odds? Is it that odd to think that your team has about a 20% chance to win?