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Comment on "Bayesian additional evidence for decision making under small sample uncertainty".

Samuel Pawel1, Leonhard Held2, Robert Matthews3

  • 1Department of Biostatistics, University of Zurich, Zurich, Switzerland. samuel.pawel@uzh.ch.

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|May 25, 2022
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Summary
This summary is machine-generated.

Bayesian Additional Evidence (BAE) offers a simple way to assess new findings. However, this study finds BAE lacks a clear rationale and practical clarity for dependable decision-making.

Keywords:
Advocacy priorAnalysis of credibilityBayesian additional evidenceReverse-Bayes

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Area of Science:

  • Statistics
  • Decision Theory
  • Information Theory

Background:

  • The study critically evaluates the recently proposed Bayesian Additional Evidence (BAE) metric.
  • It aims to provide a clear understanding of BAE's utility in scientific research.

Discussion:

  • The research derives straightforward, closed-form expressions for calculating BAE.
  • It compares BAE's characteristics against established methods for evaluating evidence.

Key Insights:

  • While computationally simple, BAE's underlying justification is not compelling.
  • The metric's lack of clarity hinders its reliable application in decision-making processes.

Outlook:

  • Further research is needed to refine BAE or develop alternative metrics.
  • Future work should focus on enhancing the interpretability and robustness of evidence assessment tools.