Cited by 1,466Statistical Methods in EpidemiologyMeta-analysis and systematic reviewsStatistical Methods in Clinical Trials

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Sifting the evidence---what's wrong with significance tests? Another comment on the role of statistical methods

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Sterne, J. A C·2001·BMJ

DOI 10.1136/bmj.322.7280.226PMC PMC1119478

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Abstract

The findings of medical research are often met with considerable scepticism, even when they have apparently come from studies with sound methodologies that have been subjected to appropriate statistical analysis. This is perhaps particularly the case with respect to epidemiological findings that suggest that some aspect of everyday life is bad for people. Indeed, one recent popular history, the medical journalist James Le Fanu's The Rise and Fall of Modern Medicine , went so far as to suggest that the solution to medicine's ills would be the closure of all departments of epidemiology.1 One contributory factor is that the medical literature shows a strong tendency to accentuate the positive; positive outcomes are more likely to be reported than null results.2–4 By this means alone a host of purely chance findings will be published, as by conventional reasoning examining 20 associations will produce one result that is “significant at P=0.05” by chance alone. If only positive findings are published then they may be mistakenly considered to be of importance rather than being the necessary chance results produced by the application of criteria for meaningfulness based on statistical significance. As many studies contain long questionnaires collecting information on hundreds of variables, and measure a wide range of potential outcomes, several false positive findings are virtually guaranteed. The high volume and often contradictory nature5 of medical research findings, however, is not only because of publication bias. A more fundamental problem is the widespread misunderstanding of the nature of statistical significance. #### Summary points P values, or significance levels, measure the strength of the evidence against the null hypothesis; the smaller the P value, the stronger the evidence against the null hypothesis An arbitrary division of results, into “significant” or “non-significant” according to the P value, was not the intention of the …

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Authors: Sterne, J. A. C.
Affiliations: University of Bristol (GB)
Corresponding author: Sterne, J. A C
ORCID: Sterne, J. A C
Venue: BMJ
Publisher: BMJ
Published: 2001-01-27
Volume: 322
Issue: 7280
Pages: 226-231
ISSN: 0959-8138 (ISSN-L)
DOI: 10.1136/bmj.322.7280.226
PMC: PMC1119478
OpenAlex: W2025651303
Cited by: 1,466
References: 48
Citations by year: 2026: 48 · 2025: 64 · 2024: 75 · 2023: 63 · 2022: 63 · 2021: 82 · 2020: 72 · 2019: 82 · 2018: 75 · 2017: 79 · 2016: 74 · 2015: 48 · 2014: 56 · 2013: 50 · 2012: 53
FWCI: 45.33
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: BMJ (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
HighWire Press Open Archive (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: statistical significance, p-values, publication bias, false positives, null hypothesis, evidence evaluation
MeSH terms: Bayes Theorem, Data Interpretation, Statistical, Humans, Research Design, Statistics as Topic, History, 20th Century

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