A friend sent me this article about Dutch social psychologist Diederik Stapel, who “perpetrated an audacious academic fraud by making up studies that told the world what it wanted to hear about human nature.” What caught my attention was this comment about how the fraud was noticed:
He began writing the paper, but then he wondered if the data had shown any difference between girls and boys. “What about gender differences?” he asked Stapel, requesting to see the data. Stapel told him the data hadn’t been entered into a computer yet.
Vingerhoets was stumped. Stapel had shown him means and standard deviations and even a statistical index attesting to the reliability of the questionnaire, which would have seemed to require a computer to produce. Vingerhoets wondered if Stapel, as dean, was somehow testing him. Suspecting fraud, he consulted a retired professor to figure out what to do. “Do you really believe that someone with [Stapel’s] status faked data?” the professor asked him.
When Zeelenberg challenged him with specifics — to explain why certain facts and figures he reported in different studies appeared to be identical — Stapel promised to be more careful in the future.
How hard is it to invent data? The same thing occurred to me with regard to Jan Hendrik Schön, a celebrated
Dutch (not that I’m suggesting anything specific about the Dutch…) [update: German, as a commenter has pointed out. Sorry. Some of my best friends are Dutch.] materials scientist who was found in 2002 to have faked experimental results.
In April, outside researchers noticed that a figure in the Nature paper on the molecular-layer switch also appeared in a paper Science had just published on a different device. Schön promptly sent in a corrected figure for the Science paper. But the incident disturbed McEuen, who says he was already suspicious of results reported in the two papers. On 9 May, McEuen compared figures in some of Schön’s other papers and quickly found other apparent duplications.
I’m reminded of a classic article from the Journal of Irreproducible Results, “A Drastic Cost Saving Approach to Using Your Neighbor’s Electron Microscope”, advocating that researchers take advantage of the fact that all electron micrographs look the same. It printed four copies of exactly the same picture, with four different captions: One described it as showing fine structure of an axe handle, another said it showed macrophages devouring a bacterium. When it comes to plots of data (rather than photographs, which might be hard to generate de novo) I really can’t see why anyone would need to re-use a plot, or would be unable to supply made-up data for a made-up experiment. Perhaps there is a psychological block against careful thinking, or against willfully generating a dataset, some residual “I’m-not-really-doing-this-I’m-just-shifting-figures-around” resistance to acknowledging the depths to which one has sunk.
Certainly a statistician would know how to generate a perfect fake data set — which means a not-too-perfect fit to relevant statistical and scientific models. Maybe there’s an opportunity there for a new statistical consulting business model. Impact!
Update: Of course, I should have said, there’s an obvious bias here: I only know about the frauds that have been detected. They were unbelievably amateurish — couldn’t even be bothered to invent data — and still took years to be detected. How many undetected frauds are out there? It’s frightening to think about it. Mendel’s wonky data weren’t discovered for half a century. Cyril Burt may have committed the biggest fraud of all time, or maybe he was just sloppy, and we may never know for sure.
I just looked at the Wikipedia article on Burt, and discovered a fascinating quote from one of his defenders, psychologist Arthur Jensen that makes an appropriate capstone for this post:
[n]o one with any statistical sophistication, and Burt had plenty, would report exactly the same correlation, 0.77, three times in succession if he were trying to fake the data.
In other words, his results were so obviously faked that they must be genuine. If he were trying to fake the data he would certainly have made them look more convincingly real.