Of course, if you look at what's happened to baseball since "Moneyball" and the success of the first statistical analysis guys, it should be a reminder that statistical prognostication is still about the

Unfortunately, our brains don't deal that well with probabilities. We don't think in probabilities. Because we're dealing with a (mostly) binary situation, we assume that as soon as the probabilities tilt in our favor, it means that a "win" is somehow assured, and mentally, the probabilities turn into a prediction. It's very, very difficult for our brains not to think that way.

So I'm thrilled to see statistical analysis "win" over the moronic pundit-class who thinks that "storylines" or "momentum" (or, um, the ultimate in believing in anecdotes over data, "my friends see more yard signs" for one candidate) are valid methods for prognosticating. But it seems that the press, by going on to insist that Silver and his ilk are the new magic prognosticators, are missing the point just as much as those who thought the election could be predicted by political pundits.

Statistics is a tool for highlighting the probabilities. I'm sure that Nate Silver clones are going to be appearing a lot more on TV during the next major election cycles -- and I think that's a step forward. But now it seems like some people are expecting Silver and other stats guys to be right every time. And that's going to lead to backlash, just as the "failure" of Moneyball-type analysis to always get it exactly right resulted in some backlash in baseball. There will be data analysis in future election cycles -- likely from Silver himself -- that is wrong. That's the nature of probabilities. It will happen. And, unfortunately, people will then suddenly go back to arguing the opposite: that the stats geeks were "wrong."

But, as they say in the stats world, these are small sample size issues. Believing that statistical analysis is a perfect tool for predictions based on a

Hopefully, as with baseball, after a few years, the whole idea that these are entirely separate worlds will melt away. In baseball, every team now uses detailed statistical analysis as

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So, the government argued, there was a 99.997% chance that the guy, Gregory Parks, must have known that the SSNs he was using came from real people, and thus, he was guilty of knowingly using their SSNs, against the law. But Parks and his lawyers went a little deeper, and pointed out that the original calculation was wrong, in that it way over-simplified things:

While amusing, this does raise a few points. First of all, it highlights how ridiculous it is to use Social Security Numbers as identifiers, given just how easy it is to guess legit SSNs. Second, it makes you wonder why the law dealing with identity fraud cares one way or another if the fake SSN was used "knowingly" or not. The guy still was guilty of mail fraud -- so it's not like he gets off completely free. But does it make sense that the laws on identity fraud only apply if you know that the SSN you're using is someone else's, but doesn't apply if you just make it up?The first three digits of a social security number are known as "area numbers." These numbers correlate to states. All of the numbers Parks used had Texas or Louisiana area numbers. Except for two: one had an Oklahoma area number and the other a Michigan area number. Area codes are published on the SSA website.

The SSA also publishes on its website information indicating the extent to which the second pair of digits in a social security number -- the "group number" -- have been assigned. In Parks' case, this information indicated that, for the 13 social security numbers he used in the Texas and Louisiana area codes, the two-digit "group number" was 99, meaning that nearly all of those numbers had been assigned. Louisiana and Texas were the areas hardest hit by Hurricane Katrina.

The group numbers for the two other area numbers used by Parks indicated that the social security numbers for those areas were not assigned to such an extent. For area number 446 (Oklahoma), the group number was 19 (out of a possible 99); for area number 372 (Michigan), the group number was 31 (again, out of 99).

All of this extra information dramatically increased Parks' odds of randomly guessing valid social security numbers. According to the court, the new math looked like this:

1 * 1 * 1 * 1 * 1 * 1 * 1 * 1 * 1 * 1 * 1 * 1 * 1 * 0.59 * 0.65 = .38

Thus, with a little knowledge about how the SSA doles out social security numbers, Parks had a 38 percent chance of "randomly" choosing 15 valid social security numbers.

According to the court's math. And that was the math that counted here. The court ruled that the high odds of making 15 educated guesses about social security numbers was sufficient to vacate Parks' conviction

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Apparently, this program has attracted $3.1 million in new deposits, many (the article claims) from people who have never been able to save much money. In many ways it is like buying a lottery ticket, except that you don't lose the money paid for the ticket. The credit unions make this work by paying out a slightly lower interest rate on the CD in question, but the net effect works out to benefit everyone. Many who put their money into such an account would never have put their money into a higher rate CD in the first place. In some ways, it's a neat example of efficient price discrimination that expands an overall market.Psychologists have long known that people tend to overestimate the odds of rare events. Applying that behavioral insight, finance professor Peter Tufano of Harvard Business School has devised a clever program called "Save to Win." Launched earlier this year for members of eight credit unions in Michigan, it is a cross between a certificate of deposit and a raffle ticket. Members who put $25 or more into a Save to Win one-year CD are entered into a monthly "savings raffle" for prizes up to $400, plus one annual drawing for a $100,000 jackpot.

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