A hugely important application of conditional probability are diagnostic tests (medical, anti-doping, quality). A test is never perfect: it can give false positives or false negatives. From here arise three key quantities — sensitivity, specificity and precision — and the celebrated “paradox of rare tests”, in which an apparently excellent test becomes unreliable for a rarely occurring illness. Bayes’ theorem is the tool that lets us calculate what really interests the patient.