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Cited by 8Statistical Methods in EpidemiologyStatistics Education and MethodologiesMeta-analysis and systematic reviews

Statistical errors in scientific research: A narrative review

Open access
Darling, HS·2024·Cancer Research, Statistics, and Treatment
DOI 10.4103/crst.crst_283_23
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Abstract

Statistics is the backbone of scientific research. Statistical errors are an inevitable part of any study. A researcher needs to be aware of various types of statistical errors, their implications, and steps to minimize them. Essentially, statistical errors may creep into medical literature either by deriving false-positive or false-negative interpretations during analysis or through fallacies in data collection. Manipulation of statistical errors to derive one’s preferred conclusions from a study is unethical. We searched medical literature in databases such as PubMed, Embase, and Cochrane for the articles published between July 2014 and June 2023 followed by abstract screening which led to the study of 8 full-length articles to gather the pertinent material. This review highlights many of the common statistical errors along with examples and suggested corrective actions.

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Crossref · 10.4103/crst.crst_283_23

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In meta-analyses, additional errors surface, such as inappropriate model selection, biased study selection, dependency, and ignoring incomplete data in selected studies.

    “In meta-analyses, additional errors surface, such as inappropriate model selection, biased study selection, dependency, and ignoring incomplete data in selected studies.”

    From Artificial Intelligence in Detecting Statistical Errors: Implications for Authors, Reviewers, and Editors (2025) · cites this paper as [4]

  • Common statistical errors in published research include inappropriate use of the t-test and Pearson's bivariate correlation, errors in logistic and linear regression analyses, incorrect reporting of P values solely, and incorrect reporting of medians and means and interquartile ranges.

    “Common statistical errors in published research include the inappropriate use of the t-test and Pearson’s bivariate correlation, errors in logistic and linear regression analyses, the incorrect reporting of P values solely, and the incorrect reporting of medians and means and interquartile ranges.”

    From Artificial Intelligence in Detecting Statistical Errors: Implications for Authors, Reviewers, and Editors (2025) · cites this paper as [4]

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Authors
Darling, H.
Affiliations
Department of Medicine, 5 Air Force Hospital, Jorhat, Assam, India
Corresponding author
Darling, HS
ORCID
Darling, HS
Venue
Cancer Research, Statistics, and Treatment
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2024-04
Volume
7
Issue
2
Pages
241-249
ISSN
  • 2590-3225 (ISSN-L)
  • 2590-3233
DOI
10.4103/crst.crst_283_23
OpenAlex
W4400005881
Cited by
8
References
6
Citations by year
2026: 3 · 2025: 5
FWCI
0.53
Type
journal article
Language
en
Open access
Licence
cc-by-nc-sa
Locations
Cancer Research Statistics and Treatment (publishedVersion, cc-by-nc-sa)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords
statistical errors, research misconduct, false positives, false-negative results, data collection errors, statistical fallacies, research ethics, medical literature

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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