title: Bayesian interpretation and analysis of research results.
abstract: From a computational perspective, Bayesian methods can be viewed as a natural extension of familiar confidence intervals and significance tests, which sheds light on their meaning. This viewpoint shows that no special software is required to compute Bayesian results, leaving the distinctions between conventional and Bayesian analyses in the conceptual realm. Key Bayesian concepts may be grasped more easily than those required for proper use of conventional methods. These concepts allow one to re-examine results from a Bayesian perspective, to complement and prevent misinterpretation of conventional results. Thus, even if frequentist results remain the norm for presentation, the inclusion of Bayesian perspectives in teaching and analysis is strongly recommended.
authors.affiliation: Department of Epidemiology and Department of Statistics, University of California, Los Angeles, CA 90095, USA. lesdomes@ucla.edu
authors.forename: Sander
authors.initials: S
authors.lastname: Greenland
mesh_terms: D001499:Bayes Theorem; D016022:Case-Control Studies; D003625:Data Collection; D006801:Humans; D007938:Leukemia / Q000453:epidemiology / Q000532:radiotherapy; D015233:Models, Statistical; D012106:Research