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Comment by School-Cotton | Hacker News Reader

Nate Silver said Trump had a 1 in 3 chance, which basically means one shouldn’t be surprised no matter the result. I’m not sure where this “all the polls were so far off in 2016!!” narrative comes from, but it’s wrong.

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It comes from innumerate journalism, and an innumerate population. Next time someone laughs off being bad at math, you should point out that being unable to read is no laughing matter, and being unable to understand numbers shouldn't be either.

The only sensible way to predict probabilities that aren't extreme is to tell people how the model works and the figures it is currently spitting out. That's is the great thing about these kinds of blog posts, people are kicking the tyres, not just looking at the car.

Nobody predicting a one-off election with a rather special candidate would summarize a 33% chance as equivalent to having no chance.

> Next time someone laughs off being bad at math, you should point out that being unable to read is no laughing matter, and being unable to understand numbers shouldn't be either.

You're not wrong, but you should not do this.

The narrative comes from the medias inaccurate and misleading coverage of the polls in 2016. Many news outlets all but declared Clinton president before the election.

But the media is not Nate Silver. He said Trump had about as much chance of winning as the Cubs had that year of wining the World Series, and obviously both happened.

Silver did a nice writeup of the whole experience: https://fivethirtyeight.com/features/the-real-story-of-2016/

That isn't the only thing that happened. Probably the 1 in 3 odds were too low given the data available, because the polling demographics were not adjusted for education. If you randomly sample 1000 people to represent several millions, you also collect demographic information to ensure that you properly weight the responses based on how skewed that demographic is in your sample compared to the total voting population. In 2016 they weren't correcting for education, which turned out to be a huge hidden variable. This is explained quite well by 538 themselves: https://fivethirtyeight.com/videos/polling-101-what-happened...

And in particular any claim 538 was the site that was off the mark compared to other prediction sites is clearly based in a reality that is not shared with the rest of us. In the week before the election Nate and crew were posting articles specifically outlining the non-zero probability of a Trump win and if it happened how it was likely to happen.

Nate was the outlier in that respect. But it’s true that the polls aren’t weren’t all that inaccurate in 2016: a bunch of important swing states were within the margin of error and Trump won some important states by very small margins.

The mistake in 2016, IMO was a) the extrapolation that came from those polls and b) people paying way too much attention to national polls, which have very little connection to electoral outcomes, given the electoral college.

Also perhaps c) the larger public not “getting” statistics in the way they’ve been presented. The NYT had, if I recall, Clinton at 90% chance of winning. That still means that in one of every ten flips of a coin is a Trump win. But people read “90% chance” as “definite win”. I don’t actually know what anyone should or could do about that.

Silver's 2012 book "The Signal and the Noise" discusses our inability to rationally process probability, pointing out that commercial weather forecasts (e.g. Accuweather) never list a probably of rain under 20-25%. A 5% chance of rain is a mathematical possibility but people "feel" like 5% = "will never happen" and get angry if it rains.

Nobel Prize winner Daniel Kahneman's life's work is about this, what he calls "System 1" and "System 2" of our brain, where System 1 is a fast responder that provides insta-feedback but is largely incapable of processing mathematical inputs. His 2011 book "Thinking Fast and Slow" summarizes his work well.

I'm not sure popular media can be trained to frame statistical probabilities in a way that doesn't provide people with the certainty they crave. But who knows?

What Nate Silver got right in 2016 were the correlations in the Rust Belt, which were traditionally considered Democrat. 538's model predicted that losing one of those states would likely mean losing all of them for Clinton, because for example the polling errors were likely correlated. And indeed losing there is what cost her the election

I think it’s a confusion between the likelihood of winning, no matter by how many votes, and the predicted percentage of votes per candidate. The latter is more commonly presented to readers from polls. So it’s not too surprising if it gets mixed up with the former, which is used by Nate et al and uses also percent as the unit.

Say a national poll predicts 55% of votes for Clinton, 40% for Trump. Whereas 538 predicts 70% chance of winning for Clinton and 30% for Trump. It’s easy to confuse the two and think the second prediction is much better for Clinton when it might be much worse.

> But people read “90% chance” as “definite win”. I don’t actually know what anyone should or could do about that.

538 are aware of the problem, and combating it with a cartoon fox (and better visualisations).

I don't know if this statement is serious. How does a cartoon fox make me think differently about the numbers?

Like the coyote in El Viaje Misterioso de Nuestro Jomer, the cartoon fox tells you only just enough to light the path towards statistical enlightenment. You must walk it yourself.

I was mostly joking; the real improvement is the better visualisations. The cartoon fox is just there as a reminder.

It was more like 1 in 5.

It's still up:

https://projects.fivethirtyeight.com/2016-election-forecast/

28.6% is between 1/3 and 1/4, definitely not 1/5.

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