\nFor Preregistration in Fundamental Research<\/a>
Discover Blog <\/font><\/strong>
By Neuroskeptic
April 25, 2013<\/div>\n<\/p>\nRecently, cognitive science postdoc Sebastiaan Mathôt wrote two pieces that raise questions about the idea of reforming scientific communication to involve preregistration of experiments: The Pros and Cons of Preregistration in Fundamental Research<\/a><\/strong> and also The Black Swan<\/a><\/strong>. Registration has long been a favorite topic of mine; it’s something I’ve been advocating since my very first post. Now it’s starting to become a reality which I think is great. Yet many researchers are wary of the idea, and Mathôt makes some important points. My answer in a nutshell is that preregistration does seem scary, in the context of science’s current culture – but that’s a problem with the current culture.<\/p>\n
Mathôt’s core argument, as I understand it, is this [from the first article, emphasis mine]:<\/div>\n<\/strong><\/sup> <\/p>\n
<\/p>\n
My colleagues and I recently conducted an experiment in which we recorded eye movements of participants while they viewed photos of natural scenes. On half of the trials we manipulated the scene based on where participants were looking. The other half of the trials served as a control condition…<\/div>\n[Our manipulation] turned out not to have the predicted effect. According to the rules of preregistration, this means that our study was worthless: We made a prediction, it didn’t come out, and any attempt to use this dataset for another purpose borders on scientific fraud.<\/strong><\/p>\n
However, we stumbled across an unexpected, but interesting and statistically highly reliable phenomenon in the control trials. So what now? Are we not allowed to look at this effect, because we did not predict it in advance? Should we run a new study, in which we predict what we have already found, and use only the data from the new experiment?<\/p>\n
Your intuition, no doubt, screams ‘no’, or at least mine does. However, the logic behind pre-registration says ‘yes’. The essential conflict here is that pre-registration discourages exploratory research, and assumes that a finding is not a real finding unless it was predicted – a questionable assumption at best.<\/div>\n<\/strong><\/sup><\/ul>\n
<\/p>\nIn this example, the authors have made two discoveries: 1] the originally predicted phenomenon didn’t happen [‘negative’]; and 2] a different, unpredicted phenomenon was observed [‘positive’]. Both of these are interesting findings, and both ought to be published. Number 1] is interesting, because the authors surely had good reasons to predict that the effect would happen. So the fact that it didn’t is a discovery; it tells us about the world, if only by narrowing down the possibilities. It contributes to science. Under the current publishing system, however, this interesting finding might never be made public – and even worse, might be regarded as deserving<\/em> to remain unpublished. Then there’s 2], the incidental positive observation. This should also be made public – and there’d be no barriers to doing so under a system of preregistration, albeit ‘only’ if it’s clearly marked as <\/em>an incidental observation. <\/em>Being incidental is not a bad thing – but you do need to be honest about it.<\/p>\n
If it sounds bad, to scientists today, it’s because we’ve been disguising our incidental findings for so long. We write papers to make ‘positive’ results seem predicted even when they weren’t – just as we make ‘negative’ findings disappear. By making such manipulation impossible, preregistration would liberate both the unexpected finding, and the negative finding. There would be a lot more of both kinds of result out there, if nothing else; I suspect their status would rise accordingly.<\/p>\n
I’ll return to this sentence of Mathôt which I think is a very clear description of a common worry: “According to the rules of preregistration, [not finding the predicted effect] means that our study was worthless.” <\/strong>The worry here is that a good experiment would be ‘wasted’ if the primary prediction turns out to be false. But the truth is that it’s the current <\/em>system that measures a study’s worth by its p-values.<\/p>\n
Preregistration is the dream that one day, studies will be judged, not by the significance of their Results, but by the content of their Methods.<\/font><\/div>\n<\/strong><\/sup><\/div>\n<\/div>\n<\/blockquote>\n
A fundamental<\/em> key to this article may be the word Fundamental<\/font><\/strong> in the title. Neuroskeptic’s talking about basic research articles, not Clinical Trials. But the more general topic is honesty and transparency in the scientific literature – how to insure it. Preregistration essentially is the idea of laying out what you’re going to do, what data is going to be collected, how it’s going to be analyzed, and setting the criteria for success or failure. Mathôt argues that preregistration stifles creativity and exploration [which it does]. Neuroskeptic counters that it would be fine to talk about exploratory findings also after directly reporting on the study as preregistered. <\/div>\nTheir dialog in the comments is cordial, each bringing up one side or the other with good points all around. There was a time when I would’ve come down on the side of Mathôt<\/font><\/strong>, ten times out of ten. I don’t much care for confining regulations and procedures, jumping through hoops, bureaucratic layers complexifying everything under the sun, proving one’s integrity. I would’ve seen this as the task of the peer review process and the editor – freedom of creativity for we high-minded scientists. But after three years of scanning Oransky’s Retraction Watch and five years haunting the psychiatric literature, particularly the clinical trial literature, I’m on Neuroskeptic<\/font><\/strong>‘s bench for this one, ten times out of ten. We’ve got preregistration for clinical trials already, and it hasn’t been followed very well and it hasn’t stopped the bleeding, but it has helped and it will help more in the future.<\/p>\n
Paxil Study 329 and Paxil Study 352 come immediately to mind. There was a protocol for each. Each published paper stated that the primary outcome variables weren’t met, and then the silver-penned Sally Laden spun her magic and there they sit in our literature as testimonies to something rotten. The STAR*D $35 M study had a thick detailed protocol book<\/em>, and in spite if 100+ published papers, the primary outcome variables have never<\/u> seen the light of day. So preregistration doesn’t necessarily hold people in the road or guarantee integrity, but it’s a step in the right direction.<\/p>\n
The stakes are too high these days – grants, promotion, tenure, prestige, fame and fortune. So how about preregistration, and<\/u> accessible raw data, and<\/u> rigorous peer review, and<\/u> integrity in the editorial process, and<\/u> retraction of distorted scientific articles? And if you run across something exciting off the path in paper #1 like in Mathôt<\/font><\/strong>‘s example, mention it in #1 then move on to paper #2 with some additional confirming or replicating data. What we’ve seen in the clinical trial world from respected scientists is more than enough justification for the hassle and stodginess of preregistration, ten times out of ten…<\/p>\n
\n…and by the way, this is a line for all times, "Preregistration is the dream that one day, studies will be judged, not by the significance of their Results, but by the content of their Methods.<\/font><\/strong>"<\/p>\n