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Updated: Feb 6, 2026

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Beyond the p Value Dichotomy: Alternatives for Statistical Inference-A Critical Review
Matheus Hissa Lourenço Ferreira1, Lucas Caseri Câmara2, Nelson Carvas Junior3,4
1Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil.
Journal of Evaluation in Clinical Practice
|February 4, 2026
Summary
The p value
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.
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