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> And of course... the forecast modelers know this. Which is what Gelman discusses in his post. Given that they now have 2016, and 2020 as examples of president
by JeremyNT 6y ago
> And of course... the forecast modelers know this. Which is what Gelman discusses in his post. Given that they now have 2016, and 2020 as examples of presidential polling errors, modelers need to shift their priors in a number of ways to account for what looks like a persistent undersampling of GOP voters.
In the article, he mentions the simplest and probably most correct way to do this: reduce the confidence these models have in the accuracy of the polling. The top line number he threw out as feeling "good" about was 75% for a Biden win.
As it is, it's becoming difficult to find any value in these models because the events they represent as outliers seem to be consistently occurring. On the other hand, if they adjust to more accurately reflect the uncertainty in the the polls, eventually everything starts looking close to a coin toss - in which case they have no practical value, either.