Massaging data to fit a theory is antithetical to science

I have stayed out of the Wansink saga for the most part. If you don’t know what this is about, I suggest reading about this case on Retraction Watch. I had a few private conversations about this with Nick Brown, who has been one of the people instrumental in bringing about a whole series of retractions of Wansink’s publications. I have a marginal interest in some of Wansink’s famous research, specifically whether the size of plates can influence how much a person eats, because I have a broader interest in the interplay between perception and behaviour.

But none of that is particularly important. The short story is that considerable irregularities have been discovered in a string of Wansink’s publications, many of which has since been retracted. The whole affair first kicked off with a fundamental own-goal of a blog post (now removed, so posting Gelman’s coverage instead) he wrote in which he essentially seemed to promote p-hacking. Since then the problems that came to light ranged from irregularities (or impossibility) of some of the data he reported, evidence of questionable research practices in terms of cherry-picking or excluding data, to widespread self-plagiarism. Arguably, not all of these issues are equally damning and for some the evidence is more tenuous than for others – but the sheer quantity of problems is egregious. The resulting retractions seem entirely justified.

Today I read an article on Times Higher Education entitled “Massaging data to fit a theory is not the worst research sin” by Martin Cohen, which discusses Wansink’s research sins in a broader context of the philosophy of science. The argument is pretty muddled to me so I am not entirely sure what the author’s point is – but the effective gist seems to shrug off concerns about questionable research practices and that Wansink’s research is still a meaningful contribution to science.  In my mind, Cohen’s article reflects a fundamental misunderstanding of how science works and in places sounds positively post-Truthian. In the following, I will discuss some of the more curious claims made by this article.

“Massaging data to fit a theory is not the worst research sin”

I don’t know about the “worst” sin. I don’t even know if science can have “sins” although this view has been popularised by Chris Chamber’s book and Neuroskeptic’s Circles of Scientific Hell. Note that “inventing data”, a.k.a. going Full-Stapel, is considered the worst affront to the scientific method in the latter worldview. “Massaging data” is perhaps not the same as outright making it up, but on the spectrum of data fabrication it is certainly trending in that direction.

Science is about seeking the truth. In Cohen’s words, “science should above all be about explanation”. It is about finding regularities, relationships, links, and eventually – if we’re lucky – laws of nature that help us make sense of a chaotic, complex world. Altering, cherry-picking, or “shoe-horning” data to fit your favourite interpretation is the exact opposite of that.

Now, the truth is that p-hacking,  the garden of forking paths, flexible outcome-contingent analyses fall under this category. Such QRPs are extremely widespread and to some degree pervade most of the scientific literature. But just because it is common, doesn’t mean that this isn’t bad. Massaging data inevitably produces a scientific literature of skewed results. The only robust way to minimise these biases is through preregistration of experimental designs and confirmatory replications. We are working towards that becoming more commonplace – but in the absence of that it is still possible to do good and honest science.

In contrast, prolifically engaging in such dubious practices, as Wansink appears to have done, fundamentally undermines the validity of scientific research. It is not a minor misdemeanour.

“We forget too easily that the history of science is rich with errors”

I sympathise with the notion that science has always made errors. One of my favourite quotes about the scientific method is that it is about “finding better ways of being wrong.” But we need to be careful not to conflate some very different things here.

First of all, a better way of being wrong is an acknowledgement that science is never a done deal. We don’t just figure out the truth but constantly seek to home in on it. Our hypotheses and theories are constantly refined, hopefully by gradually becoming more correct, but there will also be occasional missteps down a blind alley.

But these “errors” are not at all the same thing as the practices Wansink appears to have engaged in. These were not mere mistakes. While the problems with many QRPs (like optional stopping) have long been underappreciated by many, a lot of the problems in Wansink’s retracted articles are quite deliberate distortions of scientific facts. For most, he could have and should have known better. This isn’t the same as simply getting things wrong.

The examples Cohen offers for the “rich errors” in past research are also not applicable. Miscalculating the age of the earth or presenting an incorrect equation are genuine mistakes. They might be based on incomplete or distorted knowledge. Publishing an incorrect hypothesis (e.g., that DNA is a triple helix) is not the same as mining data to confirm a hypothesis. It is perfectly valid to derive new hypotheses, even if they turn out to be completely false. For example, I might posit that gremlins cause the outdoor socket on my deck to fail. Sooner or later, a thorough empirical investigation will disprove this hypothesis and the evidence will support an alternative, such as that the wiring is faulty. The gremlin hypothesis may be false – and it is also highly implausible – but nothing stops me from formulating it. Wansink’s problem wasn’t with his hypotheses (some of which may indeed turn out to be true) but with the irregularities in the data he used to support them.

“Underlying it all is a suspicion that he was in the habit of forming hypotheses and then searching for data to support them”

Ahm, no. Forming hypotheses before collecting data is how it’s supposed to work. Using Cohen’s “generous perspective”, this is indeed how hypothetico-deductive research works. In how far this relates to Wansink’s “research sin” depends on what exactly is meant here by “searching for data to support” your hypotheses. If this implies you are deliberately looking for data that confirms your prior belief while ignoring or rejecting observations that contradict it, then that is not merely a questionable research practice, but antithetical to the whole scientific endeavour itself. It is also a perfect definition of confirmation bias, something that afflicts all human beings to some extent, scientists included. Scientists must find protections from fooling themselves in this way and that entails constant vigilance and scepticism of our own pet theories. In stark contrast, engaging in this behaviour actively and deliberately is not science but pure story-telling.

The critics are not merely “indulging themselves in a myth of neutral observers uncovering ‘facts'”. Quite to the contrary, I think Wansink’s critics are well aware of the human fallibility of scientists. People are rarely perfectly neutral when it pertains to hypotheses. Even when you are not emotionally invested in which one of multiple explanations for a phenomenon might be correct, they are frequently not equal in terms of how exciting it might be to confirm them. Finding gremlins under my deck would certainly be more interesting (and scary?) than evidence of faulty wiring.

But in the end, facts are facts. There are no “alternative facts”. Results are results. We can differ on how to interpret them but that doesn’t change the underlying data. Of course, some data are plainly wrong because they come from incorrect measurements, artifacts, or statistical flukes. These results are wrong. They aren’t facts even if we think of them as facts at the moment. Sooner or later, they will be refuted. That’s normal. But this is a long shot from deliberately misreporting or distorting facts.

“…studies like Wansink’s can be of value if they offer new clarity in looking at phenomena…”

This seems to be the crux of Cohen’s argument. Somehow, despite all the dubious and possibly fraudulent nature of his research, Wansink still makes a useful contribution to science. How exactly? What “new clarity” do we gain from cherry-picked results?

I can see though that Wansink may “stimulate ideas for future investigations”. There is no denying that he is a charismatic presenter and that some of his ideas were ingenuous. I like the concept of self-filling soup bowls. I do think we must ask some critical questions about this experimental design, such as whether people can be truly unaware that the soup level doesn’t go down as they spoon it up. But the idea is neat and there is certainly scope for future research.

But don’t present this as some kind of virtue. By all means, give credit to him for developing a particular idea or a new experimental method. But please, let’s not pretend that this excuses the dubious and deliberate distortion of the scientific record. It does not justify the amount of money that has quite possibly been wasted on changing how people eat, the advice given to schools based on false research. Deliberately telling untruths is not an error, it is called a lie.

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