I don’t know about this... Forecasts in general can definitely be wrong. Assigning a 99% chance to Clinton winning, for example, was a modeling error. If you don’t have the outcome in your prediction interval, from a modeling standpoint you very likely did something wrong.https://twitter.com/zeynep/status/1323649467015376896 …
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I think what he means was the observed result: a shift of like 3 points to Trump, was not a 1-100 event.
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I think the chances of Trump winning 306 EVs was 0% in the model Morris is referring to
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model was off vs 'wrong'
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If you’re predicting vote share then you can look at whether your intervals allowed for what actually occurred. I agree there’s no final precise way to evaluate, but there is lots of room to reason about quality of models.
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No doubt about the quality, and yes, if you ruled something out. But, I'm increasingly firmly convinced whatever asterisk forecasters have to try to explain what probability models are, public still hears prediction that can be falsified. They think 538 was "wrong" in 2016.
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Maybe not wrong. But "very suspicious"
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There are degrees of wrongness. See prediction error curves: http://search.r-project.org/library/pec/html/pec.html …
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If you build a model that predicts 5 million deaths from COVID over the next month, but we only have 50k, is it wrong?
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If you say it's 100% likely that will happen, yes.
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There was more certainty than was implied by the volatility of the polls. Something this volatile should be closer to 50-50.pic.twitter.com/9Qw8JZlVuH
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