transcript
show notes
In this episode of SERious Epidemiology, Hailey and Matt are joined by Lucy D’Agostino McGowan to discuss Chapter 10 of Causal Inference: What If? and the problem of random variability. This episode explores the distinction between random error and systematic bias, what confidence intervals and standard errors actually tell us, and why increasing sample size can improve precision without making a biased estimate any closer to the truth. Lucy offers a statistician’s perspective on why random error may actually be the easier problem, and why epidemiologists should be more concerned about where an estimate is centered than about squeezing out a little more efficiency. We also discuss topics like replication, matching versus weighting, baseline adjustment in randomized trials, p-values in Table 1, and the persistent temptation to overinterpret statistical significance.
