Despite more than a decade since my own exposure to Bayes’ Theorem3,4 during medical school, its concepts still ring true in my day-to-day practice of emergency medicine (EM). For review, the theorem utilizes pretest probability to help clinicians interpret results of testing. Afterall, each medical test has a set of unique test performance that includes sensitivity, specificity, and likelihood ratio (LR). With test characteristics and disease/community context in mind, pretest probability can even be used to decide if testing is indicated in the first place! Most medical specialties involved in patient evaluation, workup, and diagnosis apply these conceptual frameworks to some degree in their daily practice. What’s unique for EM clinicians is that the same concepts apply not only to diagnostic testing, but also to facets of history-taking and physical examination. As an experienced EM physician, if an interview question or physical exam maneuver is unlikely to change my workup and treatment of a patient, I often defer it.