Beyond the p Value Dichotomy: Alternatives for Statistical Inference—A Critical Review

TL;DR

This paper reviews the limitations of using p-values as a binary measure of significance and suggests alternatives like effect sizes and Bayesian methods for better statistical inference.

Contribution

The paper provides a critical review of p-value limitations and advocates for alternative statistical methods to improve scientific inference.

Findings

38 out of 46 reviewed articles criticized the dichotomous use of p-values.

Most studies recommended abandoning the term 'statistically significant' and using compatibility intervals and effect sizes.

No article supported p-values as a standalone criterion for scientific decisions.

Abstract

The p value has long been used as the primary criterion for statistical significance; however, its dichotomous interpretation has been increasingly criticized for oversimplifying uncertainty and distorting scientific inference, particularly in health and sports sciences.

This study aimed to critically analyze the limitations of using the p value as the central criterion of statistical significance and to discuss more robust methodological alternatives for statistical inference.

A critical review was conducted using the PubMed/MEDLINE database covering the period from 2015 to 2025, complemented by citation tracking. Reviews, editorials, guidelines, and methodological essays that directly addressed the interpretation of p values and complementary metrics were included. A total of 46 articles were selected and evaluated using a self‐developed critical appraisal checklist.

Among the included studies, 38 (82.6%) explicitly criticized the isolated or dichotomous use of the p value, whereas eight adopted a more moderate position, supporting its use only when combined with confidence intervals, effect sizes, or Bayesian approaches. No article defended the p value as a standalone criterion for scientific decision‐making. The most frequent recommendations involved abandoning the term “statistically significant,” prioritizing the estimation of effect magnitude and precision, and promoting the use of compatibility intervals, effect sizes, and Bayesian methods.

Overcoming the binary logic of p < 0.05 is essential to enhance transparency, reduce bias, and better align statistical practice with the scientific and clinical relevance of research findings, particularly in the health and sports sciences.

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