5 ms·

The difference? Sample size. In baseball, everyone has the same data. It's all the data available. There's no sampling going on; it's a projection from past

• spicyj 14y ago
  > to combine multiple polls to increase the effective sample size and shrink confidence intervals How does this work? I was under the impression that to shrink the confidence interval by half you need 4x as many sample points, but there must be more sophisticated statistical methods at work here.
  • 3JPLW 14y ago
    His models have a memory (increase sample size by using a handful of day or week old data) and aggregate as many as 5 to 8 independent polls per state plus as many as a dozen national polls that factor into his simulations. That's easily four to sixteen times the sample size.
  • niggler 14y ago
    You can improve effective sample size by eliminating extraneous variables. One thing that Nate does is take advantage of implicit biases (for example, PPP polls lean slightly left)
    • waterlesscloud 14y ago
      PPP seems to have predicted 332 EV and correctly picked the winner in every state they polled. They didn't poll some of the obvious states...
• _delirium 14y ago
  I agree with that, but that's part of why I'm not convinced of his overall model's necessity: I think he gets the vast majority of his predictive power simply by being a good poll aggregator, who lays a reasonable statistical model over the polls. His model has a bunch of other stuff too, though: estimates of how current jobs numbers effect the election, "state fundamentals" based on demographic variables fit to historical election data, etc. I'm not sure these things are adding much predictive power, especially given that they're generally fit to sparse, heterogeneous, and noisy data sources (e.g. the 11 Presidential elections 1968-2008). On the other hand, one difficulty in evaluating it is that a good portion of the work his model is doing is not aimed at giving the point estimate, but the distribution and probabilities, to give an accurate probabilistic picture of the current state of the election at any given point, integrating various available data and historical trends. The goal, which is sensible, is to take a more rigorous approach to numbers you hear thrown around like, "when an incumbent is up by x% y weeks before the election, they win z% of the time". But even now in retrospect we can't easily evaluate how good those distributions were: we can see how well his point estimate fared, but with only one instance of how the election turned out, can't evaluate his overall picture's accuracy.
  • waterlesscloud 14y ago
    I'm not sure his model adds much in itself, but... I think he adds tremendous value in his write-ups. He explains what he's doing in pretty good detail, and provides some insight into what he believes it means. If you're seriously into it, you'll find places you disagree with him (same as it works in any other field when people are into it), but even if you're just casually interested you'll come away with some additional understanding. Or at the very least some additional questions of your own. And he manages to write it in such a way that general audiences can pick up some of it, and come away with the impression that there's a lot more there. Not saying it's not a true impression, just that the fact that they get the impression at all is what matters.
    • jimbokun 14y ago
      I believe Nate Silver is, by far, the best popularizer of statistical thinking in the U.S.