I think he does a better job at emphasizing the uncertainty while still showing that polls can be pretty reliable.
So, 538 did actually change their error estimate during the 2016 campaign to better account for problems of correlation. From a purely mathematical standpoint that is kinda the wrong thing to do, but it is arguably better in line with what the readers expect an error estimate to be.
Was it? What's the likely hood that someone who was polling as well as Clinton losing? 15% like The Upshot at the NY Times had? ~7% like with Sam Wang's model? Even 7% is around 1 in 14, not something shockingly improbable.
People often act like Silver's prediction was good because it gave Trump a higher probability of winning than many of the others, but that's not how probability works. If you say there's a 1/6 chance of rolling a die and getting a 2, and I say there's a 5/6 chance of rolling a 2, and we roll and get a 2, that doesn't mean that I'm correct. I don't think we've had enough rolls of elections resembling 2016 to really have a good grasp of where the percentages should be.
In general I question the value of assigning probabilities to election outcomes. This is especially true when you look at the probabilities a few months earlier - for instance, 538 had Clinton going from 49.9% on July 30, 2016 to 88.1% on October 18, 2016. Look at the probabilities they gave during the recent Democratic primaries, and they're also very bouncy. These probabilities lead people to believe that there's a much better understanding of the state of the race than what actually exists, to the point where I'd argue it edges up against pseudoscience.
Therefore I think it's fair to say that the weight an election forecast assigns to the actual winner is a direct indicator of the accuracy of its model. We aren't trying to guess at how a set of dice are weighted, knowing they'll only be thrown once—we're trying to get as close as we can to knowing who is going to vote and who they are going to vote for, and (absent some large disaster or upheaval) a misforecast will be largely attributable to systematic errors in our methodology.
Why was it a fundamental failure? A 5% chance is one in twenty, it happens.
95% is “Obama vs a dog”. Maybe in another country. Every 20 elections, Obama is bitten by the dog and doesn’t make it.
That's what it was, don't forget how impossible it seemed at the time, it was a giant upset and shock.
> You think if Hillary went up against Trump, America would choose her 19 times out of 20?
If you are using "Hillary" and "Trump" figuratively about future elections with similarly matched candidates, then yes, see above.
Only in your filter bubble.
I wouldn't take his not being prepared as evidence of anything.
I'm not saying she ran a bad campaign (though she did). I'm talking about her "negatives". Decades of scandals. (Yeah, Trump had them too, but at a minimum it meant that Hillary couldn't use Trump's scandals against him. Also, Hillary's scandals got a lot more national coverage when they happened than Trump's did.) Benghazi. The email server (and with it, the impression that she thought that rules were for other people). The impression that she thought that she was owed the presidency, rather than having to earn it. The way the DNC chose her over Sanders, overruling the will of many of the primary voters. And on and on.
It wasn't obvious at the time, because much of the press was pro-Hillary. But she was a terrible candidate. I think if the Democrats had run anyone else, they probably would have won against Trump.
Biden isn't disliked as much as Hillary was. In that sense, he's a better candidate. (Trump is disliked as much as Hillary was, and then some. Perhaps that was your point.) But Biden doesn't generate any enthusiasm, except that he's "not Trump". (In fairness, some of the support for Trump was that he wasn't Hillary.)
1. Trump is no longer an unknown.
2. Biden is far more popular than Clinton was at her peak.
3. Biden is male (which is sadly very relevant in US elections).
4. We are no longer in the biggest economic expansion in US history. We may be in a historic recession.
5. There is a pandemic that has killed 60x the number of Americans that died on 9/11, and it is no under control in the US at all.
6. Kamala Harris is not like Tim Kaine.
7. Lots of anti-Trump voters are afraid to be complacent this time.
8. Suburban women, true independent voters (those who dislike both candidates), and older Americans are either breaking for Biden or have substantially switched to Democratic support after going for Trump in 2016.
A better question would be: is anything the same this time around?
4. But the biggest (longest, anyway) economic expansion was largely under Obama. That wasn't a reason to vote for Trump in 2016.
The rest of your list I agree with, with the possible exception of 8. I'm not sure that we can tell. I think the climate is so polarized, and the Democrats have the microphone to such a degree, that people who support Trump aren't willing to say in public that they do so. But you could in fact be right. We'll see.
For #4 the economic growth was under Obama but it meant that at the time of the election people were (mostly) fat and happy; the cost of taking a chance seemed to be lower and it was easier to tell yourself that 'this guy is a businessman, he could not fuck up this strong economy and might even do better.' Now people can see what a catastrophic mistake it was to make this assumption, but I can almost understand the reasoning. I think the point of claim #4 was basically that the most that any candidate could claim about the economy is that they would have recovered it better or done it faster -- another important claim that could be made about it would be that you would make sure that the 'right people' got more benefits from the rising economy and not those elites; the basic populist economic playbook.
Either they spin it that they were perfectly correct in those 95% of cases where they get it right, or they spin it that they were the least wrong (and therefore the most correct) in the other 5% of cases where everyone gets it wrong.
There's a joke that you should always express 60% confidence in your predictions, since if the prediction pans out, you can claim to be right, but if it fails, you can bring attention to the "two times out of five wrong" part.
There is not enough data to back-test their model on a single election which happens every 4 years, so the claim that a 60% prediction is fundamentally very different from a 95% prediction is statistically dubious.
[ADDED: Or maybe something like that really is a few percent probability and you can end up with a 95% probability anyway. It just feels as if there's some upper limit to what you can measure using polls.]
who is "he" in your last sentence? ty
The page is still up, you don’t have to pull that number from memory. At the end, it was also nowhere near 95-98%. https://projects.fivethirtyeight.com/2016-election-forecast/