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METHODS article

Front. Educ.

Sec. Assessment, Testing and Applied Measurement

Beyond the p < 0.05 Trap: The Adaptive Integrity Model for Preventing and Detecting P-Hacking

Provisionally accepted
  • University of Nevada, Department of Educational Psychology, Las Vegas, United States

The final, formatted version of the article will be published soon.

When statistical significance becomes the currency of publication, the incentive to reach p < 0.05 can subtly shape research behavior. While many researchers report their findings with integrity, others face implicit pressures that increase the likelihood of selective reporting or post hoc adjustments. This article introduces the Adaptive Integrity Model (AIM), a conceptual framework that synthesizes explanation, prediction, and detection to illuminate how p-hacking tendencies arise within specific institutional and cognitive environments. Whereas existing tools such as p-curve and z-curve offer retrospective diagnostics based on p-value distributions, AIM complements these by embedding detection within a broader model of behavioral and structural influences. Its explanatory component quantifies the structural incentives and psychological biases that shape research behavior. Its predictive component flags statistical irregularities such as clustering near the significance threshold, omitted test reporting, and boundary inflation. Its detection component evaluates transparency through replication outcomes, preregistration adherence, and analytic completeness. Validated across five real-world datasets, including studies later retracted or disputed, AIM generates a Pintegrity score that captures statistical anomalies and contextual vulnerabilities. By modeling research integrity as a layered system, AIM offers journal editors, funders, and reviewers a scalable tool for credibility assessment that promotes retrospective research audits and prospective safeguards.

Keywords: academic integrity, p-value distributions, P-curve, publish or perish, Z-curve

Received: 29 Jul 2025; Accepted: 18 Nov 2025.

Copyright: © 2025 Affognon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Don A. Affognon, affognon@unlv.nevada.edu

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