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Comment by Houshalter | Hacker News Reader

After the election, Silver said something to the effect of this. That given the polling data available, there is no way anyone could have favored Trump. Clinton was leading by a large amount in a large percentage of polls. You would have to apply some seriously creative statistics to make a model that favored Trump.

Systemic polling error is a thing that happens. It's happened in previous elections. The polls are much more accurate than chance, but they aren't infallible. Particularly in this election, it's difficult to predict voter turnout from polls.

538 was one of the only models that took that into account. And as far as I know, they gave better odds to Trump than everyone else that tried to predict the election with statistical methods. Certainly they have a better track record than political pundits, which have never been better than chance.

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Yep. And it's also worth remembering that FiveThirtyEight's thing is that they do analysis based on data as opposed to punditry. Yes, they apply corrections and otherwise filter the data sources. But some people seem to think that Silver should have gotten up a couple days before the election, state that he had a bad feeling about things, and dismiss the best forecast he could make based on some shaky polling data and gone with Trump based on gut feel. Sure, he would have ended up being correct. That would also run counter to the fundamental philosophy of the site.

People forget that Silver's approach is basically weighting polls to figure out what the election will be, and he's the best "poll weigher" out there. But if the polls are systematically wrong, being the best at it is an unimpressive subtlety to the layperson.

To the extent 2018 brings increasingly contentious elections, we will likely see polling to remain inaccurate due to voter unwillingness to state unpopular opinions. In that case, multi-input "big data" approaches may prevail.

(Of course, it's also possible that 2016 is a "top" in terms of divisive rhetoric...only time can tell.)

>In that case, multi-input "big data" approaches may prevail.

What do you think this would look like? Inferring voter preferences based on proxies or instruments?

there's also the truism that if your model predicts a 25% chance for something, then 25% of the time the thing should happen.

Even if the model said 95% for Clinton, there's still a small possibility for Trump to win. It's hard to accept because 95% is really high! But hey, people win the lottery, right?

I am curious if there is a true statistically significant number of people who provide false answers and if it is growing.

plus do polls take into account where they are polling, some areas are hostile to one party or another and they may not get a good response regardless of intent

Trafalgar Group asked voters who their "neighbour" is voting for with some good outcomes. https://projects.fivethirtyeight.com/2016-election-forecast/...

This was intended to pick up on the Shy Trump voters.

I don't think it was that hard to see that there might be some bias in the polling data itself. Trump seemed very much like a candidate which would get a nontrivial number of votes from people who would not "admit" that in a poll. I'd say he's exactly the kind of candidate that has people that will very loudly say they voted for him or keep completely quiet about it and less of a middle ground.

Granted hindsight etc.

I'd say the same will be true for some of the middle-right wing parties in Europe. Notably I think AfD in Germany will (unfortunately) get more votes than polls will indicate in the upcoming election(s). Le Pen in France could be a similar case but I'm not well informed enough on French politics to feel sure about that statement.

They gave the actual outcome - >300 electors - the odds of 1 in 10. That's a bigger error than people attribute to them based on "odds of Trump winning: 1 in 3".

This is a rare case where the "data-driven" cult is forced by reality to realize how worthless everything it praises actually is, because data doesn't measure the right thing. Normally people simply ignore the difference between what's measured and what you think you're measuring and all the wrong data-based conclusions become religion every civilized person is expected to believe in. That's why it's important to drive home the point how badly 538 did (and yes, others did even worse, but 538 didn't do "pretty well", it did tremendously badly.)

And BTW there were better "data-driven" predictions than 538's, just not based on polls. Allan Lichtman's model is one that got it right. Incidentally, the incentive to outright falsify polling data (not only for pollsters but to some degree, for those polled!) is much larger than the incentive or the ability to falsify the kind of data his model looks at.

Finally, someone who worked at 538: "It sometimes seemed as though 538's interpretation of the math wasn’t free from subjective bias" (https://www.theguardian.com/commentisfree/2016/nov/09/polls-...)

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