Each race is only sampled once. But he runs the same methodology on hundreds of races. If the candidates he gives an X% chance to win go on to win approximately X% of the time, the methodology is reliable.
This is such tired debate that comes up whenever 538 is mentioned in discussion. Polls sample an election's outcome many times throughout the campaign. Statistics works. You can't know the future but you can predict it with error bars. 51/49 or 70/30 should tell you there's a very real chance of a Trump victory.
People get bent out of shape about 538 but it's usually because they're misinterpreting the prediction, not that the prediction is meaningless.
it does but just because nate silver calls something "statistics" doesn't necessarily mean that it is. If Donald Trump has a 20% chance of winning that means that we can hold the election 100 times and expect that he'll win approximately 20 elections and lose the other 80. Which is ridiculous because each voter is not an independent random variable.
Each eligible voter is a random variable, independence aside. Silver's model does not operate on the level of the voter however. A random variable is a very flexible abstraction, almost anything can be a random variable if you can find a way to measure it. Polls are a measurement. They're imperfect, but all measurements contain error.
It's not statistics because any authority says it is, it's statistics because it starts with a measure of probability (aggregated polls) and builds an interpretation on top of it. That's what statistics is. It can be good statistics or bad statistics, but that's a separate question. Incidentally, it has a pretty good track record.
If you have a criticism of Silver's model I'm curious to hear it. However, as it is I don't see a criticism in your comment. You say it's ridiculous, but it isn't. It's complicated, not ridiculous. I can't say precisely what's on your mind, but if I had to guess, it doesn't make sense to you, which is a different matter.
I don't see the point in anything that isn't falsifiable, and in order for that to happen there needs to be a sample size greater than one. If Hillary has a 99.9% chance of winning and Donald has a 0.1% chance of winning, does that mean the model was wrong or does that mean we're in the 0.1% timeline?
If you want to prove that a coin flip has a 50% chance of landing heads, a 50% chance of landing on tails, and a negligible chance of landing on its edge, you can run as many tests as you want and observe that as N approaches infinity the number of heads converges on 0.5N and the number of tails converges on 0.5N. Alternatively, you might find that the coin isn't well-balanced, in which case you've proven that the "50/50 model" was not accurate.
You can't do that with elections because each election only happens once. Even if the same two candidates are running against each other in every election, the issues at stake are different and the voterbase is different. In reality one candidate has a 100% chance of victory and the other has a 0% chance of victory but we don't know which candidate it is.
I think there’s a stronger argument that those polls were intended to suppress Trump voters into thinking they were not the majority (when in fact they were).
I think there's also a huge misunderstanding of statistics in the US.
More than one person I've spoken to believed that when sites said things like "Hilary has a 70% chance of winning" that that was the same as getting 70% of the vote, i.e. a landslide.
I've had literal arguments with people who can't/won't understand the difference between an average and a median even when presented with 5th grade level phrasing examples.
That poll was in August of 2016. A lot changed between then and election day. Obviously we can't know if Clinton really would have had a 15 point lead on the day that poll was taken, because no official vote was taken. You may think it's implausible that it could have been true, but you're just some rando on the internet, what do you know? (Same with me!)
> intended to suppress Trump voters into thinking they were not the majority (when in fact they were
Depends on what you mean by "majority". If you're just talking about Wisconsin and that specific poll, sure Trump voters ended up being in the majority, but we can't say with any certainty what that situation was in early August. Polls like that one are the best guesses we have for that particular snapshot in time.
If you're talking about an overall majority of those who ended up being actual voters, then that's also not the case, as Trump did not win a majority of votes; Clinton did. Sadly, though, popular vote majority is not how our presidential election system works.
>I think there’s a stronger argument that those polls were intended to suppress Trump voters into thinking they were not the majority (when in fact they were).
Show your work. What evidence do you have to support that argument?
He won the popular vote in 2016 in States like Wisconsin that for months said he was 10-15% behind in the polls. It’s my interpretation that those polls were wrong but the majority of the media didn’t care. They just wanted to push a narrative that supporting Trump was a fringe minority in those States, rather than the true majority that it was.
This is not an accurate description of the magnitude of polling error in 2016 in WI, which was the state with the most significant polling error that year by a very large margin, see Table 2 "2016 final polling average versus actual results": https://centerforpolitics.org/crystalball/polling-error-in-2...
The final difference from polling in WI was 7%, other states had smaller polling errors. The fact is that 2016 was simply an extremely tight race in many states and difficult to forecast even with completely normal polling error bars.
