Evidence map›Paper›PMID 38106615›Full record

ReviewPatterns (New York, N.Y.)2023

The roles, challenges, and merits of the p value.

Oliver Y Chén, Julien S Bodelet, Raúl G Saraiva, Huy Phan, Junrui Di, Guy Nagels, Tom Schwantje, Hengyi Cao, Jiangtao Gou, Jenna M Reinen and 4 more

Abstract readReview
In one paragraph

Review in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. [Interpretation of effect measures in clinical research: A practical guide for the clinician].Revista medica del Instituto Mexicano del Seguro Social · 2026
    Review
  3. Article
  4. Reporting of second-generationContemporary clinical trials communications · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Authors' response.The Indian journal of medical research · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Oliver Y ChénDépartement Médecine de Laboratoire et Pathologie, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
Julien S BodeletDépartement Médecine de Laboratoire et Pathologie, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
Raúl G SaraivaDepartment of Molecular Microbiology and Immunology, Johns Hopkins University, Baltimore, MD, USA.
Huy PhanDepartment of Computer Science, Queen Mary University of London, London, UK.
Junrui DiDepartment of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
Guy NagelsSt. Edmund Hall, University of Oxford, Oxford, UK.
Tom SchwantjeDepartment of Economics, University of Oxford, Oxford, UK.
Hengyi CaoInstitute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA.
Jiangtao GouDepartment of Mathematics and Statistics, Villanova University, Villanova, PA, USA.
Jenna M ReinenIBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA.
Bin XiongDepartment of Statistics, Northwestern University, Evanston, IL, USA.
Bangdong ZhiSchool of Business, University of Bristol, Bristol, UK.
Xiaojun WangBirmingham Business School, University of Birmingham, Birmingham, UK.
Maarten de VosFaculty of Engineering Science, KU Leuven, Leuven, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the 18th century, the p value has been an important part of hypothesis-based scientific investigation. As statistical and data science engines accelerate, questions emerge: to what extent are scientific discoveries based on p values reliable and reproducible? Should one adjust the significance level or find alternatives for the p value? Inspired by these questions and everlasting attempts to address them, here, we provide a systematic examination of the p value from its roles and merits to its misuses and misinterpretations. For the latter, we summarize modest recommendations to handle them. In parallel, we present the Bayesian alternatives for seeking evidence and discuss the pooling of p values from multiple studies and datasets. Overall, we argue that the p value and hypothesis testing form a useful probabilistic decision-making mechanism, facilitating causal inference, feature selection, and predictive modeling, but that the interpretation of the p value must be contextual, considering the scientific question, experimental design, and statistical principles.

Identifiers

PMID38106615
PMCPMC10724370

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.