Is fabricating data worse than fabricating results? Is failing to correct a known false report more or less serious than making the false report in the first place?

Andy King writes:

I have a question for you–and, if you think it worthwhile, for your readers.

A few weeks ago, I was deposed by Harvard’s lawyers in the lawsuit between Francesca Gino and Harvard. Much of the questioning focused on my replications of research by Harvard Business School professor George Serafeim and my allegations of research misconduct against him and his coauthors.

That experience has led to a lively online debate about two questions: 1. Is fabricating data worse than fabricating results? 2. Is failing to correct a known false report more or less serious than making the false report in the first place?

At the moment, my own thinking is this: 1. Both fabricating data and fabricating results mislead readers. They are simply different paths to the same outcome and thus similarly serious. 2. Failing to correct a false report–once the authors know it is false and material–may actually be more serious. It suggests a conscious decision to leave readers with a claim the authors know to be unsupported.

Your ladder of responses to criticism also seems relevant here, especially categories 6 and 7.

Interesting. This has come up in the past, discussing the moral culpability of researchers who make errors and then avoid acknowledging them. For example this guy at the London School of Economics and Political Science, or this guy at the University of Chicago, or, of course, this guy at the University of California. I don’t think that the first two of those people did any direct research misconduct, but they made major research errors that they never acknowledged–they keep pointing to their discredited work without any note of the problems–and, yeah, that seems like misconduct to me.

Here’s another story for ya. Years ago I had a colleague who showed me a paper he’d just written. It read the paper and realized it had a fatal flaw–not a calculation error, but a misapplication or misunderstanding of a statistical model. I won’t go into the details here; what’s relevant to the story right now is that the paper in question had been accepted by the journal but it had not yet been scheduled for publication. This was before the era of online anything, so the paper really was still in process. I told me colleague he was lucky: he could withdraw the paper and spare himself embarrassment. (The error in the analysis was central to the result in the paper; if you got rid of the error, there was nothing to salvage, so it’s not like he could just send in a corrected version.) To my dismay, my colleague replied, No, the paper is accepted, I don’t want to lose a publication. I asked, Doesn’t it bother you to have them publish something that’s wrong?, and he replied something about the literature being self-correcting. I don’t remember the details of this conversation from decades ago, but I do remember the horrible feeling. I thought about contacting the journal to tell them not to publish, but I figured that ultimately it was their problem for accepting it.