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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: Bayes Theorem, Data Interpretation, Statistical, Diagnostic Tests, Routine, Humans, Research Design.
user
Does the Bayesian interpretation of a P-value depend only weakly on statistical power in realistic situations? Context: It is often repeated that a low P-value provides more persuasive evidence for a genuine effect if the power of the test is high. However, this is based on an argument which ignores the precise P-value in favor of simply observing whether P is less than some cut-off, and which oversimplifies the possible effect sizes. In a non-Bayesian framework, there are good reasons to think that power does not affect the evidence of a given P-value. Here I illustrate the relationship between pre-study power and the Bayesian interpretation of a P-value in realistic situations. A Bayesian calculation, using a conventional prior distribution for the effect size and a normal approximation to the sampling distribution of the sample estimate, where the datum is the precise P-value. Over the range of pre-study powers typical in published research, the Bayesian interpretation of a given P-value varies little with power.
assistant
A Bayesian analysis with reasonable assumptions produces results remarkably in line with a more simple, non-Bayesian intuition-that the evidence against the null hypothesis provided by a precise P-value should not depend on power. Final Decision: yes