It’s time for another post in which I chew on some tidbits from Yudhijit Bhattacharjee’s incredibly thought-provoking New York Times Magazine article (published April 26, 2013) on social psychologist and scientific fraudster Diederik Stapel. (You can also look at the tidbits I chewed on in part 1, part 2, and part 3.) This time I consider the question of why it was that, despite mounting clues that Stapel’s results were too good to be true, other scientists in Stapel’s orbit were reluctant to act on their suspicions that Stapel might be up to some sort of scientific misbehavior.
Let’s look at how Bhattacharjee sets the scene in the article:
[I]n the spring of 2010, a graduate student noticed anomalies in three experiments Stapel had run for him. When asked for the raw data, Stapel initially said he no longer had it. Later that year, shortly after Stapel became dean, the student mentioned his concerns to a young professor at the university gym. Each of them spoke to me but requested anonymity because they worried their careers would be damaged if they were identified.
The bold emphasis here (and in the quoted passages that follow) is mine. I find it striking that even now, when Stapel has essentially been fully discredited as a trustworthy scientist, these two members of the scientific community feel safer not being identified. It’s not entirely obvious to me if their worry is being identified as someone who was suspicious that fabrication was taking place but who said nothing to launch official inquiries — or whether they fear that being identified as someone who was suspicious of a fellow scientist could harm their standing in the scientific community.
If you dismiss that second possibility as totally implausible, read on:
The professor, who had been hired recently, began attending Stapel’s lab meetings. He was struck by how great the data looked, no matter the experiment. “I don’t know that I ever saw that a study failed, which is highly unusual,” he told me. “Even the best people, in my experience, have studies that fail constantly. Usually, half don’t work.”
The professor approached Stapel to team up on a research project, with the intent of getting a closer look at how he worked. “I wanted to kind of play around with one of these amazing data sets,” he told me. The two of them designed studies to test the premise that reminding people of the financial crisis makes them more likely to act generously.
In early February, Stapel claimed he had run the studies. “Everything worked really well,” the professor told me wryly. Stapel claimed there was a statistical relationship between awareness of the financial crisis and generosity. But when the professor looked at the data, he discovered inconsistencies confirming his suspicions that Stapel was engaging in fraud.
If one has suspicions about how reliable a fellow scientist’s results are, doing some empirical investigation seems like the right thing to do. Keeping an open mind and then examining the actual data might well show one’s suspicions to be unfounded.
Of course, that’s not what happened here. So, given a reason for doubt with stronger empirical support — not to mention the fact that scientists are trying to build a shared body of scientific knowledge (which means that unreliable papers in the literature can hurt the knowledge-building efforts of other scientists who trust that the work reported in that literature was done honestly), you would think the time was right for this professor to pass on what he had found to those at the university who could investigate further. Right?
The professor consulted a senior colleague in the United States, who told him he shouldn’t feel any obligation to report the matter.
For all the talk of science, and the scientific literature, being “self-correcting,” it’s hard to imagine the precise mechanism for such self-correction in a world where no scientist who is aware of likely scientific misconduct feels any obligation to report the matter.
But the person who alerted the young professor, along with another graduate student, refused to let it go. That spring, the other graduate student examined a number of data sets that Stapel had supplied to students and postdocs in recent years, many of which led to papers and dissertations. She found a host of anomalies, the smoking gun being a data set in which Stapel appeared to have done a copy-paste job, leaving two rows of data nearly identical to each other.
The two students decided to report the charges to the department head, Marcel Zeelenberg. But they worried that Zeelenberg, Stapel’s friend, might come to his defense. To sound him out, one of the students made up a scenario about a professor who committed academic fraud, and asked Zeelenberg what he thought about the situation, without telling him it was hypothetical. “They should hang him from the highest tree” if the allegations were true, was Zeelenberg’s response, according to the student.
Some might think these students were being excessively cautious, but the sad fact is that scientists faced with allegations of misconduct against a colleague — especially if they are brought by students — frequently side with their colleague and retaliate against those making the allegations. Students, after all, are new members of one’s professional community, so green one might not even think of them as really members. They are low status, they are learning how things work, they are judged likely to have misunderstood what they have seen. And, in contrast to one’s colleagues, students are transients. They are just passing through the training program, whereas you might hope to be with your colleagues for your whole professional life. In a case of dueling testimony, who are you more likely to believe?
Maybe the question should be whether your bias towards believing one over the other is strong enough to keep you from examining the available evidence to determine whether your trust is misplaced.
The students waited till the end of summer, when they would be at a conference with Zeelenberg in London. “We decided we should tell Marcel at the conference so that he couldn’t storm out and go to Diederik right away,” one of the students told me.
In London, the students met with Zeelenberg after dinner in the dorm where they were staying. As the night wore on, his initial skepticism turned into shock. It was nearly 3 when Zeelenberg finished his last beer and walked back to his room in a daze. In Tilburg that weekend, he confronted Stapel.
It might not be universally true, but at least some of the people who will lie about their scientific findings in a journal article will lie right to your face about whether they obtained those findings honestly. Yet lots of us think we can tell — at least with the people we know — whether they are being honest with us. This hunch can be just as wrong as the wrongest scientific hunch waiting for us to accumulate empirical evidence against it.
The students seeking Zeelenberg’s help in investigating Stapel’s misbehavior found a situation in which Zeelenberg would have to look at the empirical evidence first before he looked his colleague in the eye and asked him whether he was fabricating his results. They had already gotten him to say, at least in the abstract, that the kind of behavior they had reason to believe Stapel was committing was unacceptable in their scientific community. To make a conscious decision to ignore the empirical evidence would have meant Zeelenberg would have to see himself as displaying a kind of intellectual dishonesty — because if fabrication is harmful to science, it is harmful to science no matter who perpetrates it.
As it was, Zeelenberg likely had to make the painful concession that he had misjudged his colleague’s character and trustworthiness. But having wrong hunches is science is much less of a crime than clinging to those hunches in the face of mounting evidence against them.
Doing good science requires a delicate balance of trust and accountability. Scientists’ default position is to trust that other scientists are making honest efforts to build reliable scientific knowledge about the world, using empirical evidence and methods of inference that they display for the inspection (and critique) of their colleagues. Not to hold this default position means you have to build all your knowledge of the world yourself (which makes achieving anything like objective knowledge really hard). However, this trust is not unconditional, which is where the accountability comes is. Scientists recognize that they need to be transparent about what they did to build the knowledge — to be accountable when other scientists ask questions or disagree about conclusions — else that trust evaporates. When the evidence warrants it, distrusting a fellow scientist is not mean or uncollegial — it’s your duty. We need the help of other to build scientific knowledge, but if they insist that they ignore evidence of their scientific misbehavior, they’re not actually helping.
