Ten common statistical mistakes to watch out for when writing or reviewing a manuscript
Open accessAbstract
Inspired by broader efforts to make the conclusions of scientific research more robust, we have compiled a list of some of the most common statistical mistakes that appear in the scientific literature. The mistakes have their origins in ineffective experimental designs, inappropriate analyses and/or flawed reasoning. We provide advice on how authors, reviewers and readers can identify and resolve these mistakes and, we hope, avoid them in the future.
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Pseudoreplication has been addressed repeatedly in ecology, agriculture, psychology, and neuroscience and acknowledged as one of the most common statistical mistakes in scientific literature.
“Pseudoreplication has been addressed repeatedly in ecology, agriculture, psychology, and neuroscience and acknowledged as one of the most common statistical mistakes in scientific literature4–9.”
From A practical solution to pseudoreplication bias in single-cell studies (2021) · cites this paper as [9]
The Abstract (page not specified) calls the paper’s ten topics common statistical mistakes, and “Inflating the units of analysis—The problem” describes the error underlying pseudoreplication: treating observations within subjects as independent experimental units. The paper does not establish that pseudoreplication has been addressed repeatedly in each of ecology, agriculture, psychology, and neuroscience. (LLM, conf=0.86)
Experimental — accuracy not yet measuredApplying statistical inference to replicates that are not statistically independent without properly accounting for their correlation structure will inflate type 1 error rates and lead to spurious results.
“Here we hope to address pseudoreplication in single-cell analyses by demonstrating what a large and long-standing body of statistical literature already confirms: applying statistical inference to replicates that are not statistically independent without properly accounting for their correlation structure will inflate type 1 error rates and lead to spurious results4–6,9,25–28.”
From A practical solution to pseudoreplication bias in single-cell studies (2021) · cites this paper as [9]
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- Authors
- Makin, T. R., & Orban de Xivry, J.-J.
- Affiliations
- University College London (GB)
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Corresponding author
- Makin, Tamar R
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2019-10-09
- Volume
- 8
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC6785265
- OpenAlex
- W2979345840
- Cited by
- 238
- References
- 69
- Citations by year
- 2026: 17 · 2025: 37 · 2024: 34 · 2023: 43 · 2022: 38 · 2021: 43 · 2020: 24 · 2019: 2
- FWCI
- 17.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (215575/Z/19/Z)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Keywords
- statistical errors, research misconduct, manuscript review, experimental design flaws, research robustness, scientific writing
- MeSH terms
- Humans, Manuscripts, Medical as Topic, Statistics as Topic
About these checks
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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