Admittedly, 538 was pretty good about showing their work after. While individual events suffer from the unfalsifiability issue, 538 when Silver was around, did pretty good “how did we do for individual races/states” and compared their given odds to the actual results.
If you predict that a particular die will land on a 3-6 and it lands on a 2, then you were wrong. Predictions are occasionally wrong, that’s unavoidable in the real world. Maybe the die wasn’t fair and you should adjust your priors.
On the other hand, if you refuse to make a prediction but simply say a particular die has a >50% chance of landing above 2, then your claim is non-falsifiable. I could roll a hundred 1’s in a row, and you could say that your probability is correct and I was just unlucky. That’s why non-falsifiable claims are ultimately worthless.
Finally, if you claim that a theoretically fair die has a 2/3 probability of landing on 3-6 then you are correct, but that does not necessarily have anything to do with the real world of dice.
He said Trump had a 28% chance of winning, and Trump won. So he was also “right.” Do you see now why what you’re saying is incorrect?
If I say there is a 4 in 6 probability of a six-sided die rolling a 1-4, I’m correct, even though I’m going to be “wrong” many times. My probability is still correct, and we would verify that by rolling the die a thousand times and looking at the statistical distribution of each number coming up.
But you can’t rerun an election 1000 times to “prove” the probability.
He predicted Clinton would win. That’s the only reasonable prediction if her win probability was over 50%
If I say a roll of a 6-sided die has a >50% chance of landing on a number above 2, and after a single roll it lands on 2, was I wrong?
If anything, the problem is in the unfalsifiability of the claim.
Admittedly, 538 was pretty good about showing their work after. While individual events suffer from the unfalsifiability issue, 538 when Silver was around, did pretty good “how did we do for individual races/states” and compared their given odds to the actual results.
If you predict that a particular die will land on a 3-6 and it lands on a 2, then you were wrong. Predictions are occasionally wrong, that’s unavoidable in the real world. Maybe the die wasn’t fair and you should adjust your priors.
On the other hand, if you refuse to make a prediction but simply say a particular die has a >50% chance of landing above 2, then your claim is non-falsifiable. I could roll a hundred 1’s in a row, and you could say that your probability is correct and I was just unlucky. That’s why non-falsifiable claims are ultimately worthless.
Finally, if you claim that a theoretically fair die has a 2/3 probability of landing on 3-6 then you are correct, but that does not necessarily have anything to do with the real world of dice.
He said Trump had a 28% chance of winning, and Trump won. So he was also “right.” Do you see now why what you’re saying is incorrect?
If I say there is a 4 in 6 probability of a six-sided die rolling a 1-4, I’m correct, even though I’m going to be “wrong” many times. My probability is still correct, and we would verify that by rolling the die a thousand times and looking at the statistical distribution of each number coming up.
But you can’t rerun an election 1000 times to “prove” the probability.
Suppose I said Trump had a 72% chance of winning the same election, which Trump won. Am I also “right”?
If so, how can it be that Trump has a 28% chance of winning and a 72% chance of winning?
If not, why is he right instead of me?