You may have heard of StatCheck, an R package developed by Michèle B. Nuijten. It allows you to search a paper (or manuscript) for common frequentist statistical tests. The program then compares whether the p-value reported in the test matches up with the reported test statistic and the degrees of freedom. It flags up cases where the p-value is inconsistent and, additionally, when the recalculated p-value would change the conclusions of the test. Now, recently this program was used to trawl through 50,000ish papers in psychology journals (it currently only recognizes statistics in APA style). The results on each paper are then automatically posted as comments on the post-publication discussion platform PubPeer, for example here. At the time of writing this, I still don’t know if this project has finished. I assume not because the (presumably) only one of my papers that has been included in this search has yet to receive its comment. I left a comment of my own there, which is somewhat satirical because 1) I don’t take the world as seriously as my grumpier colleagues and 2) I’m really just an asshole…
While many have welcomed the arrival of our StatCheck Overlords, not everyone is happy. For instance, a commenter in this thread bemoans that this automatic stats checking is just “mindless application of stats unnecessarily causing grief, worry, and ostracism. Effectively, a witch hunt.” In a blog post, Dorothy Bishop discusses the case of her own StatCheck comments, one of which gives the paper a clean bill of health and the other discovered some potential errors that could change the significance and thus the conclusions of the study. My own immediate gut reaction to hearing about this was that this would cause a deluge of vacuous comments and that this diminishes the signal-to-noise ratio of PubPeer. Up until now discussions on there frequently focused on serious issues with published studies. If I see a comment on a paper I’ve been looking up (which is made very easy using the PubPeer plugin for Firefox), I would normally check it out. If in future most papers have a comment from StatCheck, I will certainly lose that instinct. Some are worried about the stigma that may be attached to papers when some errors are found although others have pointed out that to err is human and we shouldn’t be afraid of discovering errors.
Let me be crystal clear here. StatCheck is a fantastic tool and should prove immensely useful to researchers. Surely, we all want to reduce errors in our publications, which I am also sure all of us make some of the time. I have definitely noticed typos in my papers and also errors with statistics. That’s in spite of the fact that when I do the statistics myself I use Matlab code that outputs the statistics in the way they should look in the text so all I have to do is copy and paste them in. Some errors are introduced by the copy-editing stage after a manuscript is accepted. Anyway, using StatCheck on our own manuscripts can certainly help reduce such errors in future. It is also extremely useful for reviewing papers and marking student dissertations because I usually don’t have the time (or desire) to manually check every single test by hand. The real question is if there is really much of a point doing this posthoc for thousands of already published papers?
One argument for this is to enable people to meta-analyze previous results. Here it is important to know that a statistic is actually correct. However, I don’t entirely buy this argument because if you meta-analyze literature you really should spend more time on checking the results than looking what StatCheck auto-comment on PubPeer said. If anything, the countless comments saying that there are zero errors are probably more misleading than the ones that found minor problems. They may actually mislead you into thinking that there is probably nothing wrong with these statistics – and this is not necessarily true. In all fairness, StatCheck, both in its auto-comments and the original paper is very explicit about the fact that its results aren’t definite and should be verified manually. But if there is one thing I’ve learned about people it is that they tend to ignore the small print. When is the last time you actually read an EULA before agreeing to it?
Another issue with the meta-analysis argument is that presently the search is of limited scope. While 50,000 is a large number, it is a small proportion of scientific papers, even within the field of psychology and neuroscience. I work at a psychology department and am (by some people’s definition) a psychologist but – as I said – to my knowledge only one of my own papers should have even been included in the search so far. So if I do a literature search for a meta-analysis StatCheck’s autopubpeering wouldn’t be much help to me. I’m told there are plans to widen the scope of StatCheck’s robotic efforts beyond psychology journals in the future. When it is more common this may indeed be more useful although the problem remains that the validity of its results is simply unknown.
The original paper includes a validity check in the Appendix. This suggests that error rates are reasonably low when comparing StatCheck’s results to previous checks. This is doubtless important for confirming that StatCheck works. But in the long run this is not really the error rate we are interested in. What this does not tell us which proportion of papers contain actual errors with a study’s conclusions. Take Dorothy Bishop‘s paper as an example. For that StatCheck detected two F-tests for which the recalculated p-value would change the statistical conclusions. However, closer inspection reveals that the test was simply misreported in the paper. There is only one degree of freedom and I’m told StatCheck misinterpreted what test this was (but I’m also told this has been fixed in the new version). If you substitute in the correct degrees of freedom, the reported p-value matches.
Now, nobody is denying that there is something wrong with how these particular stats were reported. An F-test should have two degrees of freedom. So StatCheck did reveal errors and this is certainly useful. But the PubPeer comment flags this up as a potential gross inconsistency that could theoretically change the study’s conclusions. However, we know that it doesn’t actually mean that. The statistical inference and conclusions are fine. There is merely a typographic error. The StatCheck report is clearly a false positive.
This distinction seems important to me. The initial reports about this StatCheck mega-trawl was that “around half of psychology papers have at least one statistical error, and one in eight have mistakes that affect their statistical conclusions.” At least half of this sentence is blatantly untrue. I wouldn’t necessarily call a typo a “statistical error”. But as I already said, revealing these kinds of errors is certainly useful nonetheless. The second part of this statement is more troubling. I don’t think we can conclude 1 in 8 papers included in the search have mistakes that affect their conclusions. We simply do not know that. StatCheck is a clever program but it’s not a sentient AI. The only way to really determine if the statistical conclusions are correct is still to go and read each paper carefully and work out what’s going on. Note that the statement in the StatCheck paper is more circumspect and acknowledges that such firm conclusions cannot be drawn from its results. It’s a classical case of journalistic overreach where the RetractionWatch post simplifies what the researchers actually said. But these are still people who know what they’re doing. They aren’t writing flashy “science” article for the tabloid press.
This is a problem. I do think we need to be mindful of how the public perceives scientific research. In a world in which it is fine for politicians to win referenda because “people have had enough of experts” and in which a narcissistic, science-denying madman is dangerously close to becoming US President we simply cannot afford to keep telling the public that science is rubbish. Note that worries about the reputation of science are no excuse not to help improve it. Quite to the contrary, it is a reason to ensure that it does improve. I have said many times, science is self-correcting but only if there are people who challenge dearly held ideas, who try to replicate previous results, who improve the methods, and who reveal errors in published research. This must be encouraged. However, if this effort does not go hand in hand with informing people about how science actually works, rather than just “fucking loving” it for its cool tech and flashy images, then we are doomed. I think it is grossly irresponsible to tell people that an eighth of published articles contain incorrect statistical conclusions when the true number is probably considerably smaller.
In the same vein, an anonymous commenter on my own PubPeer thread also suggested that we should “not forget that Statcheck wasn’t written ‘just because.'” There is again an underhanded message in this. Again, I think StatCheck is a great tool and it can reveal questionable results such as rounding down your p=0.054 to p=0.05 or the even more unforgivable p<0.05. It can also reveal other serious errors. However, until I see any compelling evidence that the proportion of such evils in the literature is as high as suggested by these statements I remain skeptical. A mass-scale StatCheck of the whole literature in order to weed out serious mistakes seems a bit like carpet-bombing a city just to assassinate one terrorist leader. Even putting questions of morality aside, it isn’t really very efficient. Because if we assume that some 13% of papers have grossly inconsistent statistics we still need to go and manually check them all. And, what is worse, we quite likely miss a lot of serious errors that this test simple can’t detect.
So what do I think about all this? I’ve come to the conclusion that there is no major problem per se with StatCheck posting on PubPeer. I do think it is useful to see these results, especially if it becomes more general. Seeing all of these comments may help us understand how common such errors are. It allows people to double check the results when they come across them. I can adjust my instinct. If I see one or two comments on PubPeer I may now suspect it’s probably about StatCheck. If I see 30, it is still likely to be about something potentially more serious. So all of this is fine by me. And hopefully, as StatCheck becomes more widely used, it will help reduce these errors in future literature.
But – and this is crucial – we must consider how we talk about this. We cannot treat every statistical error as something deeply shocking. We need to develop a fair tolerance to these errors as they are discovered. This may seem obvious to some but I get the feeling not everybody realizes that correcting errors is the driving force behind science. We need to communicate this to the public instead of just telling them that psychologists can’t do statistics. We can’t just say that some issue with our data analysis invalidates 45,000 and 15 years worth of fMRI studies. In short, we should stop overselling our claims. If, like me, you believe it is damaging when people oversell their outlandish research claims about power poses and social priming, then it is also damaging if people oversell their doomsday stories about scientific errors. Yes, science makes errors – but the fact that we are actively trying to fix them is proof that it works.