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Did the analysts who caused Apple’s stock drop also cast Ashton Kutcher to play Jobs?

Every quarter, a public company’s earnings either meet analysts’s expectations or they do not. It’s one or the other.

Every four years, a Republican or a Democrat is elected President of the United States. It’s one or the other.

We Americans love binary decisions, and we love guessing how those decisions will come out, and we really love to follow those who make a living off these binary guesses.

The most famous and beloved binary guesser in America today is a reclusive little guy with bad eyesight who spends all his time holed up in the Northeast until his handlers bring him out to make his predictions. So far, he has a 39% accuracy rate.

On 50/50 bets.

We love predictions, but clearly we couldn’t care less about accuracy.

The famous and beloved binary guesser I’m talking about, of course, is Punxsutawney Phil (sorry, Nate), the Pennsylvania groundhog who comes out of his hole at the beginning of February every year to tell us what kind of spring we’re going to have. Will it be an early spring, or will we have six more weeks of winter? It’s one or the other.

And in 116 years, lovable Btfsplk Phil has called it 39% of the time. That’s what, 45 out of 116? So how bad is that? Let’s consult our friend the binomial distribution. Here’s what Wolfram Alpha says:

  • number of trials: 116

  • success probability: 1/2

  • left endpoint: 0

  • right endpoint: 46

  • p ( 0 < x < 46 ) = 0.00994

I’m guessing that Mxyzptlk Phil isn’t guessing. He knows something. You can’t be wrong this often by chance. He’s an excellent predictor; he’s just wrong most of the time.

Not like newspaper psychics, astrological charts, or political pundits. They’re just bad. (And they know it: long-time pundit Bay Buchanon announced this year that she’s quitting the prognostication racket and taking an online real estate course.)

Of course Presidential election predictions are a crap shoot. At least the kind where you look at past elections and try to extrapolate to the next one. The clinker is the paucity of data. Since 1788, Presidential elections have only generated a bit less than half the number of data points ol’ Bqhatevwr Phil has.

Fifty-six Presidential elections. That’s all you’ve got to work with, and for most of those elections you have little in the way of helpful demographic data. You can be pretty sure George Washington was elected without carrying the usually crucial Ohio electoral votes, but how did John Tyler do among urban seniors (which in 1841 probably meant people over 44)? We just don’t know.

Usually Presidential predictions based on past elections restrict themselves to even less data. When you hear, “Since World War II no one has been elected President without...” you can tell the speaker to stop right there. The bar predictors have to get over to get out of anecdote space and into statistics is about twenty data points. There are not twenty data points in the number of Presidential elections since World War II.

I’m not saying that articles on what past Presidential elections can tell us about the next one are worthless. I’m saying that their worth lies in the discipline of literature, not science.

There are things you can predict with some confidence. Like predicting that Ashton Kutcher playing Steve Jobs wasn’t going to work out well. Or that the you’d already have broken most of this year’s New Year’s resolutions by now.

And predicting where technology is going, piece of cake. Like this delicious angelfood on the future of ERP.

Then there are the people whose job it is to predict how a public company will perform in the next quarter. That’s a tough job, clearly, because—no, wait: their job is to predict how well a company will turn out to have performed in the last quarter. Huh. That seems like it would be easier. Anyway, how well do they perform? Apple makes more money than any other company in the history of the world, and its stock goes down. Why? Because the predictors predicted badly.

Those predictors suck.

At least there’s one bit of rationality that emerges from all this tea-leaf failure. It now makes perfect sense that Google has hired Ray Kurzweil to chart its course into the unknown.

But this story need a moral. How’s this: Esther Schindler passed along the fact that the guy who predicted the end of the world last year—OK, yeah, I know, that doesn’t narrow it down enough—Harold Camping, the guy who predicted that the world would end on May 21—that guy? He’s a former engineer. Esther quotes TIL: “Which just proves what we always knew: NEVER trust an engineer when he gives you a schedule.”

John Shade was born under a cloud in Montreux, Switzerland, in 1962. Subsequent internment in a series of obscure institutions of ostensibly higher learning did nothing to brighten his outlook. He confidently predicts that tomorrow he will be just as morose as today. Follow John on Twitter, send him your feedback, or discuss the article in the magazine forum.