{"id":2803,"date":"2026-03-04T23:35:42","date_gmt":"2026-03-04T23:35:42","guid":{"rendered":"https:\/\/thenewstatistics.com\/itns\/?p=2803"},"modified":"2026-03-04T23:35:46","modified_gmt":"2026-03-04T23:35:46","slug":"beyond-the-p-value-reform-spreads-across-the-world-and-across-disciplines","status":"publish","type":"post","link":"https:\/\/thenewstatistics.com\/itns\/2026\/03\/04\/beyond-the-p-value-reform-spreads-across-the-world-and-across-disciplines\/","title":{"rendered":"Beyond the p Value: Reform Spreads Across the World and Across Disciplines"},"content":{"rendered":"\n

…as evidenced by this article<\/a><\/strong> from Brazil, which I’m delighted to see:<\/p>\n\n\n\n

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The article’s header<\/strong><\/mark><\/figcaption><\/figure>\n\n\n\n

I salute Karen Grimmer<\/a><\/strong>, JECP <\/a><\/strong>co-editor, for publishing it, and for managing to make it Open Access. Karen happens to be a long-standing friend of mine who now continues the good work in ‘retirement’. I declare an interest: I was a referee for the ‘Beyond the p<\/em> Value…’ article.<\/p>\n\n\n\n

Note the innovative review, evaluation, and synthesis techniques developed by the authors. Here’s the Abstract<\/strong>:<\/p>\n\n\n\n

Rationale<\/h5>\n\n\n\n

The p<\/em> 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.<\/p>\n\n\n\n

Aims and Objectives<\/h5>\n\n\n\n

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

Methods<\/h5>\n\n\n\n

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<\/em> values and complementary metrics were included. A total of 46 articles were selected and evaluated using a self-developed critical appraisal checklist.<\/p>\n\n\n\n

Results<\/h5>\n\n\n\n

Among the included studies, 38 (82.6%) explicitly criticized the isolated or dichotomous use of the p<\/em> 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<\/em> value as a standalone criterion for scientific decision-making. The most frequent recommendations involved abandoning the term \u201cstatistically significant,\u201d prioritizing the estimation of effect magnitude and precision, and promoting the use of compatibility intervals, effect sizes, and Bayesian methods.<\/p>\n\n\n\n

Conclusion<\/h5>\n\n\n\n

Overcoming the binary logic of p<\/em>\u2009<\u20090.05 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.<\/p>\n\n\n\n

A Practical Summary<\/h3>\n\n\n\n

A particularly useful feature is a dot point summary of practical recommendations near the end:<\/p>\n\n\n\n