Abstract

The clinical process of diagnosis and treatment selection is based on reasoning that employs logical and scientific inferences to reach conclusions and make decisions. These reasonings are grounded in solid statistical foundations, within which two main approaches operate: the frequentist and the Bayesian. The frequentist approach is the most commonly used and focuses on null hypothesis significance testing and the p-value. In recent years, the evaluation of scientific evidence on the efficacy of interventions using this approach has led to controversial conclusions. As a complement, Bayesian analysis of randomized controlled trials, based on hypothesis updating through the results of clinical studies, offers a more intuitive perspective that aligns with clinical practice, potentially leading to different conclusions from those derived from the frequentist approach. This educational article examines the mathematical and clinical foundations of Bayesian analysis in randomized controlled trials, illustrating its application with a real-world example. © 2025, Sociedad Chilena de Infectologia. All rights reserved.