• David Ferreira, Mael Barthoulot, Julien Pottecher, Klaus D Torp, Pierre Diemunsch, and Nicolas Meyer.
• Anesthesiology and Intensive Care Department, CHU de Besançon, Besançon, France; Université de Strasbourg, iCUBE, UMR7357, Illkirch Cedex, France. Electronic address: dferreira@chu-besancon.fr.
• Br J Anaesth. 2020 Aug 1; 125 (2): 201-207.

AbstractThe critical reading of scientific articles is necessary for the daily practice of evidence-based medicine. Rigorous comprehension of statistical methods is essential, as reflected by the extensive use of statistics in the biomedical literature. In contrast to the customary frequentist approach, which never uses or gives the probability of a hypothesis, Bayesian theory uses probabilities for both hypotheses and data. This statistical approach is increasingly used for analyses of clinical trial data and for applied machine learning. The aim of this review is to compare general Bayesian concepts with frequentist methods to facilitate a better understanding of Bayesian theory for readers who are not familiar with this approach. The review is intended to be used in combination with a checklist we have devised for reading reports analysed by Bayesian methods. We compare and contrast the different approaches of Bayesian vs frequentist statistical methods by considering data from a clinical trial that lends itself to this comparative approach.Copyright © 2020 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.

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