Bayesian Thinking in Rehabilitation Research

OBJECTIVE: To describe key conceptual differences between frequentist and Bayesian statistical approaches and illustrate their relevance to rehabilitation research, where treatment effects are often modest and clinical decision-making occurs under uncertainty. DESIGN: Perspective article comparing inferential frameworks with emphasis on interpretation rather than mathematical formulation. Illustrative examples include neuromodulation studies and a Bayesian reanalysis of the MISTIE III trial. RESULTS: Frequentist methods, centered on P values and confidence intervals, provide established tools for indirect assessment of treatment effects and remain central to trial interpretation, but do not directly estimate the probability that a treatment is beneficial. Bayesian approaches combine prior evidence with observed data to estimate posterior probabilities, including the probability of any benefit or benefit exceeding a clinically meaningful threshold. In illustrative examples, Bayesian interpretation provided a complementary lens for characterizing uncertainty beyond binary significant/nonsignificant conclusions. CONCLUSION: Bayesian methods complement traditional analyses by providing directly interpretable probabilities of treatment benefit and supporting decision-making under uncertainty. Their conclusions depend on prior assumptions and do not replace rigorous trial design, frequentist inference, or replication.