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Retract p < 0.005 and propose using JASP, instead.

Jose D Perezgonzalez1, M Dolores Frías-Navarro2

  • 1Business School, Massey University, Manawatu Campus,P.O.Box 11-222, Palmerston North , 4442, New Zealand.

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Summary

A stricter p < 0.005 significance level is proposed to improve scientific reproducibility. However, using Bayes factors analysis alongside frequentist statistics offers a more robust approach to evaluating evidence without mixing research philosophies.

Keywords:
Bayes factorsp-valuespractical significancereplicabilityreproducibilityresearch evidencestatistical significance

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Area of Science:

  • Psychology and life sciences research
  • Scientific methodology and statistical analysis

Background:

  • Research reproducibility is a significant challenge in science.
  • A proposal suggests a stricter p < 0.005 threshold for statistical significance to enhance discovery claims.
  • This proposal has generated considerable debate among researchers.

Purpose of the Study:

  • To evaluate the proposed stricter p < 0.005 significance standard.
  • To analyze counterarguments regarding the proposal's philosophical and methodological implications.
  • To present a simpler, more suitable alternative for assessing statistical evidence.

Main Methods:

  • Critical reflection on the original p < 0.005 proposal and its critiques.
  • Exploration of Bayes factors analysis as a complementary statistical tool.
  • Discussion of integrating frequentist error statistics with Bayesian beliefs.

Main Results:

  • The stricter p < 0.005 standard faces significant philosophical and methodological challenges.
  • Bayes factors analysis provides a framework for understanding both frequentist and Bayesian perspectives.
  • Parallel use of Bayes factors analysis can enhance interpretation of statistical evidence.

Conclusions:

  • The proposed p < 0.005 threshold may not be the optimal solution for reproducibility.
  • Bayes factors analysis offers a more integrated approach to statistical inference.
  • Utilizing Bayes factors analysis (e.g., via JASP) is recommended for a nuanced understanding of research findings.