Evidence map›Paper›PMID 27489256›Full record

ReviewJournal of investigative medicine : the official publication of the American Federation for Clinical Research2016

P values: from suggestion to superstition.

John Concato, John A Hartigan

Abstract readReview
In one paragraph

Review in Journal of investigative medicine : the official publication of the American Federation for Clinical Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

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

  1. Pooled it
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  10. Calculation of the minimum clinically important difference (MCID) using different methodologies: case study and practical guide.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024
    Article
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  16. Chemical Composition and Biological Activities ofMolecules (Basel, Switzerland) · 2022
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  20. Reporting Characteristics in Sports Nutrition.Sports (Basel, Switzerland) · 2018
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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

2 authors.

John ConcatoClinical Epidemiology Research Center, Cooperative Studies Program, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
John A HartiganDepartment of Statistics, Yale University, New Haven, Connecticut, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A threshold probability value of 'p≤0.05' is commonly used in clinical investigations to indicate statistical significance. To allow clinicians to better understand evidence generated by research studies, this review defines the p value, summarizes the historical origins of the p value approach to hypothesis testing, describes various applications of p≤0.05 in the context of clinical research and discusses the emergence of p≤5×10(-8) and other values as thresholds for genomic statistical analyses. Corresponding issues include a conceptual approach of evaluating whether data do not conform to a null hypothesis (ie, no exposure-outcome association). Importantly, and in the historical context of when p≤0.05 was first proposed, the 1-in-20 chance of a false-positive inference (ie, falsely concluding the existence of an exposure-outcome association) was offered only as a suggestion. In current usage, however, p≤0.05 is often misunderstood as a rigid threshold, sometimes with a misguided 'win' (p≤0.05) or 'lose' (p>0.05) approach. Also, in contemporary genomic studies, a threshold of p≤10(-8) has been endorsed as a boundary for statistical significance when analyzing numerous genetic comparisons for each participant. A value of p≤0.05, or other thresholds, should not be employed reflexively to determine whether a clinical research investigation is trustworthy from a scientific perspective. Rather, and in parallel with conceptual issues of validity and generalizability, quantitative results should be interpreted using a combined assessment of strength of association, p values, CIs, and sample size.

Indexed as

ProbabilitySuperstitionsConfidence IntervalsGenomicsSample SizeBiostatisticsClinical ResearchResearch Design

Identifiers

PMID27489256
PMCPMC5099183

What OpenQuestion holds

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LicenceCC BY-NC
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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.