I am in general a strong supporter of the reproducibility movement and I agree it could be useful to document the extent to which the existing psychology literature contains statistical errors.However, I think there are 2 problems with how this is being done in the PubPeer study.1. The tone of the PubPeer comments will, I suspect alienate many people. As I argued on Twitter, I found it irritating to get an email saying a paper of mine had been discussed on PubPeer, only to find that this referred to a comment stating that zero errors had been found in the statistics of that paper.I don't think we need to be told that - by all means report somewhere a list of the papers that were checked and found to be error-free, but you don't need to personally contact all the authors and clog up PubPeer with comments of this kind.My main concern was that during an exceptionally busy period, this was just another distraction from other things. Chris Hartgerink replied that I was free to ignore the email, but that would be extremely rash because a comment on PubPeer usually means that someone has a criticism of your paper.As someone who works on language, I also found the pragmatics of the communication non-optimal. If you write and tell someone that you've found zero errors in their paper, the implication is that this is surprising, because you don't go around stating the obvious*. And indeed, the final part of the comment basically said that your work may well have errors in it and even though they hadn't found them, we couldn't trust it.Now at the same time as having that reaction, I appreciate this was a computer-generated message, written by non-native English speakers, that I should not take it personally, and no slur on my work was intended. And I would like to know if errors were found in my stats, and it is entirely possible that there are some, since none of us is perfect. So I don't want to over-react, but I think that if I, as someone basically sympathetic to this agenda, was irritated by the style of the communication, then the odds are this will stoke real hostility for those who are already dubious about what has been termed 'bullying' and so on by people interested in reproducibility.2. I'll be interested to see how this pans out for people where errors are found.My personal view is that the focus should be on errors that do change the conclusions of the paper.I think at least a sample of these should be hand-checked so we have some idea of the error rate - I'm not sure if this has been done, but the PubPeer comment certainly gave no indication of that - it just basically said there's probably an error in your stats but we can't guarantee that there is, putting the onus on the author to then check it out.If it's known that on 99% of occasions the automated check is accurate, then fine. If the accuracy is only 90% I'd be really unhappy about the current process as it would be leading to lots of people putting time into checking their papers on the basis of an insufficiently sensitive diagnostic. It would make the authors of the comments look frankly lazy in stirring up doubts about someone's work and then leaving them to check it out.In epidemiology the terms sensitivity and specificity are used to refer to the accuracy of a diagnostic test. Minimally if the sensitivity and specificity of the automated stats check is known, then those figures should be provided with the automated message.
I hadn't seen the 2nd paper - presumably because I was not the corresponding author on that one. It's immediately apparent that the problem is that F ratios have been reported with one degree of freedom, when there should be two. In fact, it's not clear how the automated program could assign any p-value in this situation.I'll communicate with the first author, Thalia Eley, about this, as it does need fixing for the scientific record, but, given the sample size (on which the second, missing, degree of freedom is based), the reported p-values would appear to be accurate.