P-value hacking significantly distorts published scientific literature
Extensive methodological research demonstrates that p-hacking and selective reporting practices significantly distort published scientific literature and undermine the validity of meta-analytic findings.
The retrieved literature consistently supports the claim that p-value hacking and related publication biases significantly distort published scientific literature, inflate effect sizes, and compromise research integrity. Multiple papers outline how these practices impact evidence synthesis and meta-analyses, and none refute the claim.
Bramley P, Bird C, Badgett R, DeVito NJ. Patterns of preregistration and publication of trials in Cochrane systematic reviews of interventions.. 2025. https://doi.org/10.1016/j.jclinepi.2025.111958
Notes that p-hacking and selective reporting have detrimental effects on scientific literature and evidence synthesis.
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Irsova Z, Bom PRD, Havranek T, Rachinger H. Spurious precision in meta-analysis of observational research.. 2025. https://doi.org/10.1038/s41467-025-63261-0
Demonstrates how p-hacking interacts with methodological decisions to produce spurious precision that undermines standard meta-analytic techniques.
Ferreira MHL, Câmara LC, Junior NC. Beyond the p Value Dichotomy: Alternatives for Statistical Inference-A Critical Review.. 2026. https://doi.org/10.1111/jep.70373
Highlights how dichotomous p-value interpretations distort scientific inference and oversimplify uncertainty.
Watson HJ. A Statistical Analysis Plan Template for Observational Studies: Promoting Quality and Rigor in Research.. 2025. https://doi.org/10.1007/s42519-025-00504-9
Discusses how data-driven analyses and questionable research practices like p-hacking threaten the rigor and trustworthiness of scientific findings in observational research.
van Aert RCM, van Assen MALM. Correcting for publication bias in a meta-analysis with the p-uniform* method.. 2026. https://doi.org/10.3758/s13423-025-02812-4
Explains how publication bias and related distortions threaten the validity of meta-analyses and overestimate effect sizes.
Malte Friese, Julius Frankenbach. p-Hacking and Publication Bias Interact to Distort Meta-Analytic Effect Size Estimates. 2020. https://doi.org/10.31234/osf.io/bvm58
Shows via simulation that p-hacking and publication bias interact to distort meta-analytic effect size estimates and inflate false-positive rates.
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