Comment by Tasha Fairfield

Bayesian probability provides a natural and intuitive framework for characterising and communicating uncertainty. Bayesian analysis simply applies the laws of probability to evaluate which hypothesis is more plausible in light of whatever relevant information we have, however limited. Inference takes the form of posterior odds, which express how much confidence we have in the leading hypothesis relative to rivals given the evidence in hand, or equivalently, how much uncertainty surrounds our findings—which can always change when we learn new information. Examples from the pandemic—the debate over covid origins and expert guidance on public health measures—will be used to (i) illustrate how Bayesian inference works, (ii) highlight shortcomings in expert reasoning, and (iii) call attention to the potential pitfalls of overstating confidence in a given hypothesis.