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Revised standards for statistical evidence.

Valen E Johnson1

  • 1Department of Statistics, Texas A&M University, College Station, TX 77843-3143.

Proceedings of the National Academy of Sciences of the United States of America
|November 13, 2013
PubMed
Summary
This summary is machine-generated.

Bayesian hypothesis testing offers objective methods that align with classical significance tests. Increasing evidence thresholds can improve scientific reproducibility by demanding higher significance levels.

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Area of Science:

  • Statistics
  • Bayesian Inference
  • Hypothesis Testing

Background:

  • Classical significance tests are widely used but face reproducibility concerns.
  • Uniformly most powerful Bayesian tests offer an objective alternative.
  • A correspondence exists between Bayesian and classical hypothesis testing frameworks.

Purpose of the Study:

  • To explore the connections between Bayesian hypothesis testing and classical significance testing.
  • To propose evidence thresholds for improving the reproducibility of scientific findings.
  • To link Bayesian evidence thresholds with classical significance levels.

Main Methods:

  • Equating classical test sizes with Bayesian evidence thresholds.
  • Equating P values with Bayes factors.
  • Examining the relationship between test significance levels and reproducibility.

Main Results:

  • Concerns regarding scientific reproducibility are linked to overly lenient significance testing.
  • Recommended evidence thresholds for significance are 25-50:1 and 100-200:1 for high significance.
  • These standards correspond to classical significance levels of 0.005 or 0.001.

Conclusions:

  • Adjusting evidence thresholds in Bayesian and classical hypothesis testing can enhance scientific rigor.
  • Higher significance levels are crucial for reliable and reproducible research.
  • Adopting stricter statistical standards is essential for scientific advancement.