I'm sympathetic to the case, made by @zeynep and others, that forecasting has less value when the polls regularly produce big misses.
But some forecasters, like the Nates, have avoided triumphalism and been upfront about the limitations of their models.
Others, not so much... https://twitter.com/mattyglesias/status/1324507561308114947 …
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Replying to @Yascha_Mounk @zeynep
We have written a ton about the limitations of modeling and uncertainty in the source data, including an initial post-mortem of the polls that went up last night. https://www.economist.com/united-states/2020/11/05/polls-and-our-forecast-model-overestimated-support-for-joe-biden … https://statmodeling.stat.columbia.edu/2020/11/04/post-election-post/ … https://statmodeling.stat.columbia.edu/2020/11/02/so-whats-with-that-claim-that-biden-has-a-96-chance-of-winning-some-thoughts-with-josh-miller/ … https://statmodeling.stat.columbia.edu/2020/10/28/concerns-with-our-economist-election-forecast/ …
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The whole point of modeling the election is to describe what could happen if the data were wrong. Our model said that even in the case of a polling/model error 2x the side of 2016, Biden would probably win. That is exactly what happened. I stand by how we covered the data.
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Replying to @gelliottmorris @Yascha_Mounk
I assure you that there is almost nobody out there who thinks "the whole point of modeling the election is to describe what would happen if the data were wrong." They see the 96% your model forecast, and some then act accordingly—showing up or not, splitting ticket, etc.
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Yeah, I am not at all a fan of the coverage, which I had hoped would get better after models but it got worse because models felt more "scientific" rather than, wait polls are garbage, the event is rare, what are we doing here?
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