Lies, Damned Lies, And 'Proofiness' Math is the only truly exact science, but numbers can also be used to fudge and deceive. Author Charles Seife explores the ways numbers can lie in his new book, Proofiness: The Dark Arts of Mathematical Deception.
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Lies, Damned Lies, And 'Proofiness'

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Lies, Damned Lies, And 'Proofiness'

Lies, Damned Lies, And 'Proofiness'

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So corn sugar by any other name would taste as sweet. I think we all get that words can lead or mislead, but numbers can too. Sometimes we give them a pass. Take Joseph McCarthy and his list of 205 known communists in the State Department, or was it 207 or 57 or maybe 81?

No one really knew because McCarthy made up those 205 nonexistent communists. He knew that numbers had power, just as Stephen Colbert coined the word truthiness, meaning pseudo-facts that seem true, there's now proofiness.

That's the term journalist Charles Seife came up with to describe deliberately misleading numbers. His new book is "Proofiness: The Dark Arts of Mathematical Deception." And Charles Seife joins me now from our New York bureau.


Mr. CHARLES SEIFE (Author, "Proofiness: The Dark Arts of Mathematical Deception"): Hi. Thank you so much for having me.

PESCA: You're welcome. The McCarthy example, we see it in everyday life all the time, mostly, I find, on the cover of lifestyle magazines. So they'll advertise 27 ab exercises or 245 ways to celebrate summer. And I'm saying, wow, five more ways, and you'd have a nice round number. But why in these instances is a nice round number not good for the purposes of the people putting the numbers out?

Mr. SEIFE: Well, it's funny because numbers communicate how far you can trust them. And a nice round number is signaling, well, you know, I'm in the area, that it's not a very precise number.

But if you've got a really nice precise number - 207, 58, 31 - people know that that means exact, and therefore they believe that it has some real basis in reality, that it's to be trusted.

PESCA: Let's go to something, a word that maybe you've coined, but disestimation, what's that?

Mr. SEIFE: Disestimation is the act of taking a number too seriously and trusting it far beyond the point at which you can stop trusting it.

PESCA: What's an example of that?

Mr. SEIFE: The examples of Joe McCarthy is a good example, or actually that was an outright lie.

(Soundbite of laughter)

Mr. SEIFE: But a nice anecdote I like to talk about is a guide at the American Museum of Natural History, who's pointing at the Tyrannosaurus rex.

Someone asks, how old is it, and he says it's 65 million and 38 years old. Sixty-five million and 38 years old, how do you know that? The guide says, well, when I started at this museum 38 years ago, a scientist told me it was 65 million years old. Therefore, now it's 65 million and 38.

That's an act of disestimation. The 65 million was a very rough number, and he turned it into a precise number by thinking that the 38 has relevance when in fact the error involved in measuring the dinosaur was plus or minus 100,000 years. The 38 years is nothing.

PESCA: So most of the book "Proofiness," you talk about that museum guide, and that doesn't seem like he was trying to deceive anyone or there was anything nefarious there. But proofiness does have an element of deception, in fact the subtitle, "The Dark Arts of Mathematical Deception."

Mr. SEIFE: Absolutely. The dark side of proofiness is that people who know how to use it are using mathematical techniques, mathematical deception, to convince people of things that are outright wrong.

And as a result, it sways policy. It changes the way people vote. It changes the number of voters. It dilutes your ability to communicate your will to the people in power.

PESCA: Did you come across any examples of proofiness or innumeracy or mathematical chicanery that kind of came by just honestly yet led to a bad result?

Mr. SEIFE: I think when journalists, for example, report polls, they just don't know better that they shouldn't take these results literally.

Every poll comes with a little number attached to it called the margin of error, and the margin of error is taken as how reliable this poll is, that it's 23 percent belief that Barack Obama is not born in the United States, plus or minus three percent. The problem is that for these polls, the margin of error only describes one very specific type of error that plagues polls. It comes from taking a small sample of people and projecting it to the entire world.

But in fact, when polls go wrong, it's due to a completely different type of error called the systematic error. These are errors that come when the poll isn't set up quite right, that the questions are a bit misleading or that you choose the wrong sample of people to interview or people are lying.

And these numbers are not reflected in the margin of error. So when journalists report polls, most of which are really not worth the paper they're written on, I think they're kind of innocently performing an act of proofiness, giving the public quasi-fictions in the name of fact.

And the preponderance of polls all around the news media is it's getting more and more, stronger and stronger. You can't go to a news website without seeing a poll on the front page, often an Internet poll.

Internet polls are completely meaningless, and yet you find them all over in the news media. So I think that the accessibility and the frequency of proofy numbers is fairly new. It's getting worse.

PESCA: Charles Seife, author of "Proofiness: The Dark Arts of Mathematical Deception." Thank you very much.

Mr. SEIFE: Thank you for having me.

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