A cool explanation and praise of Nate Silver's election forecasting

(via Reddit) Annmaria's Blog at thejuliagroup.com has a post up explaining how Nate Silver's election predictions worked, and why it was a really brave thing to do.   I thought this post was really cool, because I didn't quite understand any of what was going on at Silver's blog -- all I knew was that he was forecasting an Obama victory, and that was comforting.

Well, I got a little more than that.  I understand that when someone has a 90% chance of winning something, and that thing only happens once, it's still totally possible for the other person to win.  In fact, roughly one tenth of unique 90%-probability events go the way of the 10% margin.

I didn't know, though, whether Silver was partisan, or whether his math was any good.  The conversation about it was mostly over my head, and taking place in venues I don't closely follow.  (Although I've been barely following anyone in the past couple weeks -- been kind of busy and flustered.)

It turns out, there's this thing called the Central Limit Theorem, which says, according to Annmaria's Blog, 

the mean of an infinite number of reasonably large random samples will be the population mean.

No idea how you prove that, but apparently it's well-accepted statistical theory.

So, more realistically, the more reasonably large random samples, the more likely that their mean will be predictive of the actual result.  Which I think means that about 90% of polls added up to an Obama victory in the electoral college.

Annmaria's Blog (sorry I keep referring to her by her blog's title, but I can't find her name) explains that Silver's forecasting was extraordinary -- she says heroic -- not just because he applied good statistics, which many statisticians could have done, but that he put that prediction out there, took the risk of a misunderstanding public and an (at the end) 8% chance of ruining his own career, to stand up for good applied math.

I agree -- it's a heroic thing to do.  He elevated the quality of public discourse, and provided more vivid, undeniable evidence for the usefulness of data to reach conclusions.  I think his contribution will have a genuinely substantial positive effect on political discourse in the next 4 years.

Kickstarter on Growth

(Via Neil Gaiman's Tumblr) You know what's awesome?  Statistical analysis.  You know what else is awesome?  Kickstarter.  And, apparently, when those two things come together, a beautiful thing happens:  We find out that Kickstarter is even more awesome than people think.

A lot of people believe that the more Kickstarters there are, the harder it will be to get one off the ground.  Hell, there was an xkcd strip about it.  But according to Kickstarter's blog, that's not the case.

As we've grown, we've heard people worry that it will be harder and harder to fund projects as the total number of projects grows. They wonder: Do more projects mean greater competition for the same dollars? 

And when there's a blockbuster project, they ask: Are these projects stealing backers from other worthy projects?

For both questions, the opposite actually appears to be true. Projects aren't fighting over a finite pool of Kickstarter dollars or backers. One project's backer isn't another project's loss. The backers that one project brings often end up backing other projects as well. Each project is not only promoting itself, but the Kickstarter ecosystem as a whole.

The very cool charts in their blog post detail the way that big projects result in huge overflows of backers into other cool projects in the same area.  The video game example of Double Fine, especially, showed a huge positive-sum impact on the community with a successful, large project.