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The p value

Keywords:
Bayes theoremclinical decision‐makingconfidence intervalsdata interpretationevidence‐based practicereproducibility of resultssports medicinestatistical

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Area of Science:

  • Statistical inference in health and sports sciences.
  • Methodological advancements in scientific research.

Background:

  • The p value is a traditional metric for statistical significance.
  • Its dichotomous interpretation is criticized for oversimplifying uncertainty and distorting scientific inference.

Purpose of the Study:

  • Critically analyze limitations of p value as a sole significance criterion.
  • Discuss robust methodological alternatives for statistical inference.

Main Methods:

  • Critical literature review (2015-2025) from PubMed/MEDLINE.
  • Included reviews, editorials, guidelines, and essays on p value interpretation.
  • Evaluated 46 articles using a critical appraisal checklist.

Main Results:

  • 82.6% of studies criticized isolated/dichotomous p value use.
  • No article supported p value as a standalone decision criterion.
  • Recommendations include abandoning "statistically significant," prioritizing effect size, and using Bayesian methods.

Conclusions:

  • Moving beyond the binary p < 0.05 logic is crucial.
  • Enhances transparency, reduces bias, and aligns statistics with scientific relevance.
  • Essential for health and sports sciences research.