4 ms·

Yeah the cases have some striking similarity. There's generally two sources of error - bias, and noise. When you combine many measurements, only bias accumulate

Yeah the cases have some striking similarity. There's generally two sources of error - bias, and noise. When you combine many measurements, only bias accumulates. Noisy errors in one state poll or one mortgage-based cash flow will be offset by noisy errors in another. If you get surprised by a change from a low-bias regime to a high-bias one, your old assumptions will lead you to be overconfident in your model. For 538, this just meant publishing numbers that were way too confident. But in the financial context, overconfident modeling means you think you can use more leverage than you actually can.

traek 8y ago
> For 538, this just meant publishing numbers that were way too confident. This isn't the case for the 2016 elections. 538 ended up having a much more robust forecast because they accounted for systematic polling error (bias). https://fivethirtyeight.com/features/why-fivethirtyeight-gave-trump-a-better-chance-than-almost-anyone-else/ https://fivethirtyeight.com/features/why-fivethirtyeight-gav...
TheOperator 8y ago
>For 538, this just meant publishing numbers that were way too confident. 538 was just about the only polling aggregrator that WASN'T guilty of this during 2016 and were relatively bullish on Trump. Their mistake during 2016 was releasing a series of editorials not really based on data at all that predicted Trump would eventually peak in support and the remaining voters would rally behind a sensible seeming conservative establishment choice like Marco Rubio. Their theory was plausible and completely wrong. Yet their numbers consistently predicted Trump victory in the primsries as he was an eternal frontrunner for virtually the entire primary.