Bayesian Statistics for Arthroplasty Surgeons: Moving Beyond Simple "Yes or No" P-Values to Probabilistic Clinical Decision-Making

Statistical interpretation underpins evidence-based decision-making in total hip arthroplasty (THA) and total knee arthroplasty (TKA). Traditional frequentist approaches that rely on arbitrary P-value thresholds have long guided research design and analysis in arthroplasty. This approach produces binary conclusions that may overlook clinically meaningful differences, particularly for infrequent outcomes. Bayesian statistics provide a complementary framework that integrates prior knowledge with new evidence to generate probabilistic statements, which align more closely with real-world clinical reasoning. Primary prospective Bayesian-designed research remains rare in orthopaedics. Most Bayesian insights come from post hoc reanalyses of existing frequentist trials. Nevertheless, studies that employ Bayesian methods are increasing. This narrative review introduces core Bayesian principles and Bayes' theorem, illustrates the role of pretest probability in diagnostic testing with practical examples, contrasts frequentist and Bayesian interpretations, and offers criteria for critically appraising Bayesian arthroplasty research.