Too True To Be Good
The real reason you can't believe everything you read.
Steven E. Landsburg
Published in Slate Magazine,
Posted Wednesday, Sept. 15, 1999, at 4:30 p.m. PT

Not everything you read on the World Wide Web is true. Not everything you read in the New York Times is true, either. So when you read about scientific breakthroughs, how do you know what to believe?
Partly, you trust your instincts: A theory that life evolved from clay is more inherently plausible than a theory that life evolved from Play-Doh. Partly, you consider the source: A Harvard professor is more credible than a Dartmouth dishwasher. And partly you rely on expert judgments: If a prestigious journal has agreed to publish the clay theory, it's probably wrong.
Yes, I meant to say that: If a prestigious journal publishes a theory, it's probably wrong. Given two equally plausible theories from equally credible sources that have passed equally strict scrutiny, the one that makes it into a top journal has a smaller chance of being right. Here's why: Editors like to publish theories they find surprising. And the best way to surprise an editor is to be wrong.
That's not to say that editors are reckless. At least in mathematics and economics (the two fields where I can testify from personal experience), the editorial process is rigorously demanding. Long before an article is submitted for publication, the author is expected to circulate drafts among experts in the field and to respond to their criticisms and comments--a process that typically takes years. Only then is the (now heavily revised) article formally submitted, whereupon the editor handpicks an expert referee to examine it line by line--a process that can easily take another year or more. Are referees ever lax and careless? Surely. Are they lax and careless with articles of genuine importance? In my observation, essentially never. Through multiple rounds of correspondence, referees demand satisfaction regarding every important detail. In many cases, the author will visit the referee's home institution for a semester or a year to be available for periodic grilling.
That's exactly what's so damning about the hoax perpetrated in 1996 by Alan Sokal. Sokal's paper, intentionally stripped of logic, evidence, and even meaning, was accepted for publication in the cultural studies journal Social Text. True, this was a one-time event, but it was an event so far removed from anything that could possibly occur in a legitimate academic enterprise that it converted agnostics like me, who had doubted the status of cultural studies as an intellectual discipline, into hard-core cynics with no doubt whatsoever.
In a serious economics journal, it would be impossible to publish an article like Sokal's. But it would not be impossible, or even unusual, to publish a carefully reasoned article that's still wrong. That's because of the bias I mentioned earlier: Given two papers that have both survived the vetting process, editors tend to prefer the more surprising, which means that on average they prefer the one that's wrong.
It's easy to see how the same dynamic could work at a newspaper. "Man bites dog" is a better story than "dog bites man," but it's also more likely to be wrong, even if both stories are reported by equally reliable witnesses. In general--and this observation is a mainstay of college statistics courses--when you think you've seen something unusual, you're more likely to be mistaken than when you think you've seen something ordinary. But it's the unusual that makes the front page.
A few years ago, economics professors J. Bradford De Long and Kevin Lang devised an ingenious way to determine just how many published economic hypotheses are actually true. They looked through several years' worth of issues of the top economics journals and found 78 hypotheses that were confirmed by strong evidence--the sort of evidence that led the authors to accept their own hypotheses. (Another 198 hypotheses were rejected by their authors.) In exactly none of the 78 cases could the confirmation be called overwhelming.
But that's OK. Strong evidence is, after all, strong evidence, even when it's a little shy of overwhelming. In most cases, overwhelming evidence is too much to ask for, because evidence can be hard to collect and hard to interpret. So no individual article can be criticized for failing to live up to an unattainable standard.
But, said De Long and Lang, out of 78 true hypotheses, surely there should be at least a few that are overwhelmingly confirmed. In fact, they gave a precise definition of the word overwhelming, according to which roughly 10 percent of all true hypotheses should come packaged with overwhelming evidence. So if all 78 hypotheses are true, then roughly 7.8 of them--call it eight-- should be confirmed overwhelmingly. And they're not.
OK, so maybe that's because not all 78 are true. Maybe only 50 are true. In that case, five should be confirmed overwhelmingly. Or maybe only 30 are true, in which case three should be confirmed overwhelmingly. The problem is that exactly zero are confirmed overwhelmingly, and zero is 10 percent of--zero! So out of 78 "confirmed" hypotheses, it seems that approximately zero are true.
Using a more sophisticated version of the same techniques, De Long and Lang concluded that some of the 78 "confirmed" hypotheses might be true, but probably not more than about a third of them. In other words, when a published article in a top journal presents evidence that its hypothesis is true, its hypothesis is probably false. It would be very interesting to perform the same experiment with, say, medical journals instead of economics journals. I'd be very surprised if the results were substantially different.
If this makes you feel pessimistic about the progress of science, keep in mind that we can learn a lot from even a very few true hypotheses submerged in a sea of false ones. And here's another ray of hope: De Long and Lang's results were published in the prestigious Journal of Political Economy, so they're probably wrong to begin with. And this account of them was published in Slate, so it's probably wrong, too.

This paper, published in the Review of Economics and Statistics, provides a more formal explanation of how one might systematically obtain unlikely results in academic journals. The paper requires some knowledge of statistics, particularly of the meaning of Type I and Type II errors.