flynn002

Yesterday I had the pleasure of hearing George Davey Smith (aka @mendel_random) talk. In the course of a wide-ranging lecture he recounted his experiences with conducting a systematic review. This caught my interest, as I’d recently considered the question of literature reviews when writing about fallibility in science . George’s talk confirmed my concerns that cherry-picking of evidence can be a massive problem for many fields of science. Together with Mark Petticrew, George had reviewed the evidence on the impact of stress and social hierarchies on coronary artery disease in non-human primates. They found 14 studies on the topic, and revealed a striking mismatch between how the literature was cited and what it actually showed. Studies in this area are of interest to those attempting to explain the well-known socioeconomic gradient in health. It’s hard to unpack this in humans, because there are so many correlated characteristics that could potentially explain the association. The pr...

At a recent conference, someone posed a question that had been intriguing me for a while: suppose you have limited resources, with the potential to test N participants. Would it be better to do two studies, each with N/2 participants, or one big study with all N? I've been on the periphery of conversations about this topic, but never really delved into it, so I gave a rather lame answer. I remembered hearing that statisticians would recommend the one big study option, but my intuition was that I'd trust a result that replicated more than one which was a one-off, even if the latter was from a bigger sample. Well, I've done the simulations and it's clear that my intuition is badly flawed. Here's what I did. I adapted a script that is described in my recent slides that give hands-on instructions for beginners on how to simulate data, The script, Simulation_2_vs_1_study_b.R , which can be found here, generates data for a simple two-group comparison using a t-test. In...