Evidence map›Paper›PMID 32998683›Full record

ReviewBMC medical research methodology2020

Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise.

Zad Rafi, Sander Greenland

Abstract readReview
In one paragraph

Review in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 7 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

77 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  15. Exploring the proper use ofGlobal epidemiology · 2026
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  17. Giving less power to statistical power.Laboratory animals · 2025
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17 more citing papers are in PubMed but not listed here.

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.

Zad RafiDepartment of Population Health, NYU Langone Medical Center, 227 East 30th Street, New York, NY, 10016, USA. zad@lesslikely.com.ORCID 0000-0003-1545-8199
Sander GreenlandDepartment of Epidemiology and Department of Statistics, University of California, Los Angeles, CA, USA.ORCID 0000-0003-4364-3279

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundResearchers often misinterpret and misrepresent statistical outputs. This abuse has led to a large literature on modification or replacement of testing thresholds and P-values with confidence intervals, Bayes factors, and other devices. Because the core problems appear cognitive rather than statistical, we review some simple methods to aid researchers in interpreting statistical outputs. These methods emphasize logical and information concepts over probability, and thus may be more robust to common misinterpretations than are traditional descriptions.

methodsWe use the Shannon transform of the P-value p, also known as the binary surprisal or S-value s = -log

conclusionsIn line with other recent recommendations, we advise that teaching materials and research reports discuss P-values as measures of compatibility rather than significance, compute P-values for alternative hypotheses whenever they are computed for null hypotheses, and interpret interval estimates as showing values of high compatibility with data, rather than regions of confidence. Our recommendations emphasize cognitive devices for displaying the compatibility of the observed data with various hypotheses of interest, rather than focusing on single hypothesis tests or interval estimates. We believe these simple reforms are well worth the minor effort they require.

Indexed as

CognitionSemanticsBayes TheoremCohort StudiesConfidence IntervalsHumansProbabilityBiasCognitive scienceConfidence intervalsData interpretationEvidenceHypothesis testsInformationModels, statisticalP-valuesStatistical significance

Identifiers

PMID32998683
PMCPMC7528258

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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.