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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Replying to @gelliottmorris
How is it wrong? Trump had one percent chance, that model claimed, and that happened. I don't think it was a great model, but I don't see how it's wrong. One percent events happen, obviously, and we worry about them and change our whole lives over it: see COVID CFR.
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Replying to @zeynep
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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Replying to @gelliottmorris
With COVID, those models *very much* depend on behavior so they are even harder for epi models to be "wrong." It's an exponential dynamic, so yep, the range is huge. The question is were the assumptions justified? You rarely get that from outcome alone given that reflexive range.
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Replying to @zeynep @gelliottmorris
Given Trump's polls in 2016, it's pretty insane to think he had a 1% chance. People with that deficit win all the time. It was a junk model
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Replying to @FriendOfTheYeti @gelliottmorris
I do agree those models sucked, but you cannot get that from outcome alone. Any model that did not have correlated shifts taken properly into account sucks for any electoral college modeling.
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Replying to @zeynep @FriendOfTheYeti
“Sucks” is just a silly way to hedge against saying “wrong” — or, perhaps better, grossly and dangerously misspecified
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We're not going to finish this here as you note, but I do think moving away from right or wrong as the terminology is important to get to a point where we can 1-communicate what models do; 2-understand they impact outcome; 3-do evaluate them. We do agree they can be evaluated.
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All models are wrong... Some are more wrong than others.
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