Last time around, the “shy Trump voter” effect was blamed for the polling being wrong - the idea being that many Trump voters weren’t willing to be truthful to pollsters.
I don’t see how the pollsters can fix that in next year’s election either, because being a Trump voter is still as being just as socially bad as it was in 2016, if not in fact even worse. Hence, Trump voters have even more incentive to lie to pollsters than before. So are the pollsters going to add an artificial 2-4% Trump boost to their polling in order to cover the expected discrepancy?
Do you have a cite for this? In my understanding, the polling in 2016 was only slightly less accurate than usual. The national polling was quite good, and all states except for a few were fine, in my understanding. So no, I don’t expect pollsters will add any sort of boost to any candidate; doing so would likely result in reduced accuracy.
If you’re not following Nate Silver with respect to polling, you’re doing it wrong.
The polling was fine. You don’t fire a weather forecaster for being off by one degree in their forecast. Trump won by the narrowest of margins in three critical states.
And, of course, most of the polls weren’t able to take that damn Comey letter into effect. How many reluctant Clinton voters threw their hands in the air and said, ‘none of the above!’
Polling was overall quite accurate, despite inherent difficulties in this cell-phone era. The aggregate models of FiveThirtyEight, for example, accurately foresaw a rather high chance that Trump would win the electoral vote — close to 30% chance, as I recall. There was some “shy Trump” voting going on, but as I recall this was less a factor than (among other things) the portion of voters to non-voters being somewhat different than expected within certain demographic groups. Polling methods continue to evolve and improve slowly, but the basic approach needn’t change. The same goes for aggregate modeling methods — there are always lessons learned to incorporate, but the “shy Trump voter” is a small part of of all this, and no one needs to categorically add some fixed percentage to account for it.
The polling nationally was actually very accurate. The reason why pollsters were off is the fact that Trump won specific states by very thin margins, which was the difference in the race. But the polling had Clinton winning anywhere from 1-3 percent nationally, and that’s pretty much what happened. Unfortunately, she lost the EC and it was a surprise because there wasn’t nearly as much good polling data at the state level, which was one of the reasons why Nate Silver disagreed with Sam Wang and said that there was a lot more uncertainty in the polls than people believed at the time.
I don’t think there were “shy” Trump voters. More like unaccounted for Trump voters, and voters who were showing up to vote for the first time in several election cycles, and in some cases the first time ever. There were also Clinton voters who simply failed to show up at the polls. Moreover, there were 3rd and even 4th party candidates who, while receiving less than 5% of the vote, were enough to swing the races in some states.
Well, OK, sure, but what are the predictors going to do to ensure that their predictions of win % probability are better this time than in 2016?
I feel like I should have reworded my thread title to “election forecasting” instead of polling. The raw polling data itself might have been okay, but the way the forecasters predicted all-but-guaranteed victory for Hillary sure wasn’t.
You misunderstand. They ran their model a thousand times each week, and in that final week, in THREE HUNDRED out of those thousand runs, Trump straight-out WON. Reality happened to then coincide with one of those THREE HUNDRED runs. Not surprising in the least.
If there were ten identical situations — ten contests where the exact same 538 model happened to apply, and the exact same polling numbers were generated each week as they were in this instance — and the “Hillary” candidate won all ten races, THAT would indicate serious PROBLEMS with the model. You’d EXPECT her to straight-up lose around three of those times. Which she did.
The problem isn’t that polls “weren’t accurate” (it’s the predictions some made which weren’t accurate), but that people in general don’t understand statistics, (as the premise of the OP demonstrates).
When multiple sources claim a 98-99% chance of victory for Hillary, then something did in fact go wrong with the modeling or assessment.
If they had claimed a 70-80% chance of victory, one could claim that Trump simply happened to be the lucky red card drawn out of a shuffled hand that included three or four other blue cards, but when they go with a **99% **prediction for Hillary, that’s way out of mere chance.
If you have three states, each of which has a one third chance of going for trump, getting all three of them sure sounds like a real long shot, one the order of one in twenty-seven. Which does calculate out to about a three percent chance. The problem with this mode of thinking is that it regards them as discrete events, like coin tosses. Except they aren’t discrete events, they’re inter-related so that something (or someone) that helped trump in one has an effect in the others.
I have said that I think that there are trump voters who lie to pollsters, either from shame or perversity and I don’t know how you could compensate for that as even a larger sample might simple include more of them folks.
Like Manafort’s list, maybe. It would include all three.
And really, those three situations are why Trump is president. It was such a small percentage of those states, let alone the country as a whole. And yet, people took it as some kind of huge, ignored part of the country. That narrative is bullshit. The rest of Trump’s electoral wins were mostly people just automatically voting Republican, as they always do.
Given the reliability of previous polling processes in most previous elections, a sudden and massive failure is not best explained by a failure of the process.
You know about Russian hackers. You knew about Diebold in earlier elections. You know about Republican vote suppression tactics both before and after the fact. You don’t know what else you don’t know. Do you still trust that the announced vote totals accurately reflected who all the voters thought they were voting for?
Russian ‘hackers’ were trying to influence people via social media and furnishing truthful data concerning the Hillary email server. Diebold is fuzzy. Any effects of voter suppression would not likely end with a voter voting incorrectly.