The urge to sprinkle statistics is irresistible

Reading the article by Glasziou et al, we might be forgiven for believing that they had discovered some hitherto unknown method of causal inference.¹ Instead, of course, they have merely stumbled across the way in which causes have been identified in everyday life and science throughout history.²

The “mother's kiss” technique for removing a bead lodged in a nostril is an effective treatment not only because it has been shown to work in case reports but also because it is grounded in elementary principles of physics familiar to every child who has played with a pea shooter. It does not need statistical analysis. Yet, the authors—unable to free themselves of the urge to season the data with a sprinkle of relative risks or P values—neglect the fact that the many examples they provide of treatments with clearly observable effects are widely accepted without the need for statistical tricks.

The obsession with both randomised controlled trials and the statistical approach to causation has clouded the thinking of a generation or more of medical researchers. So much so, that the commonsense notion of causation has been relegated to little more than an afterthought. And this accounts for the dismissive approach to any data not derived from randomised trials. Perhaps, after their damascene conversion, Glasziou et al will campaign for a change in the hierarchy of evidence in favour of data from non-randomised sources.