Peer review of statistics in medical research: the other problem

Peer review has long been criticised for failing to identify flaws in research. Here Peter Bacchetti argues that it is also guilty of the opposite: finding flaws that are not there The process of peer review before publication has long been criticised for failing to prevent the publication of statistics that are wrong, unclear, or suboptimal. 1 2 My concern here, however, is not with failing to find flaws, but with the complementary problem of finding flaws that are not really there. My impression as a collaborating and consulting statistician is that spurious criticism of sound statistics is increasingly common, mainly from subject matter reviewers with limited statistical knowledge. Of the subject matter manuscript reviews I see that raise statistical issues, perhaps half include a mistaken criticism. In grant reviews unhelpful statistical comments seem to be a near certainty, mainly due to unrealistic expectations concerning sample size planning. While funding or publication of bad research is clearly undesirable, so is preventing the funding or publication of good research. Responding to misguided comments requires considerable time and effort, and poor reviews are demoralising—a subtler but possibly more serious cost. This paper discusses the problem, its causes, and what might improve the situation. Although the main focus is on statistics, many of the causes and potential improvements apply to peer review generally. #### Summary points Peer reviewers often make unfounded statistical criticisms, particularly in difficult areas such as sample size and multiple comparisons These spurious statistical comments waste time and sap morale Reasons include overvaluation of criticism for its own sake, inappropriate statistical dogmatism, time pressure, and lack of rewards for good peer reviewing Changes in the culture of peer review could improve things, particularly honouring good performance Mistaken criticism is a general problem, but may be especially acute for statistics. The examples below illustrate …