The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
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 literature4–9.”
From A practical solution to pseudoreplication bias in single-cell studies (2021) · cites this paper as [9]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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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Citations
Cites
Lakens, D. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00863 · Cited by 10,303
Estimating the reproducibility of psychological science
Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515
Power failure: why small sample size undermines the reliability of neuroscience
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3475 · Cited by 7,641
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011) · Psychological Science · doi:10.1177/0956797611417632 · Cited by 6,567
Using Effect Size—or Why the P Value Is Not Enough
Sullivan, G. M., & Feinn, R. (2012) · Journal of Graduate Medical Education · doi:10.4300/jgme-d-12-00156.1 · Cited by 5,553
Normality Tests for Statistical Analysis: A Guide for Non-Statisticians
Ghasemi, A., & Zahediasl, S. (2012) · International Journal of Endocrinology and Metabolism · doi:10.5812/ijem.3505 · Cited by 4,368 · cited 1×
“Another problem related to small sample size is that the distribution of the sample is more likely to deviate from normality, and the limited sample size makes it often impossible to rigorously test the assumption of normality (Ghasemi and Zahediasl, 2012).”
1,500 scientists lift the lid on reproducibility
Baker, M. (2016) · Nature · doi:10.1038/533452a · Cited by 4,209
Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602413113 · Cited by 3,487
A manifesto for reproducible science
Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E.-J., Ware, J. J., & Ioannidis, J. P. A. (2017) · Nature Human Behaviour · doi:10.1038/s41562-016-0021 · Cited by 3,397
Moving to a World Beyond “ p < 0.05”
Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019) · The American Statistician · doi:10.1080/00031305.2019.1583913 · Cited by 2,976
Flagged first, then by citations
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Sorted by citations
Items 201–239 of 239
Medina, Ú., Gordillo‐Moscoso, A., & Almendra‐Pegueros, R. (2023) · Revista de Nutrición Clínica y Metabolismo · doi:10.35454/rncm.v6n3.564
Pièges et mésusages en analyse de données
Supplisson, O., & Sofonea, M. T. (2023) · Anesthésie & Réanimation · doi:10.1016/j.anrea.2023.08.002
Statistical design of experiments: the forgotten component of Reduction
Reynolds, P. S. (2024) · Lab Animal · doi:10.1038/s41684-024-01334-1
Assessing the Rigor of Machine Learning in Physiological Signal Processing Applications
Ishmakhametov, N., Naser, M. Y. M., & Bhattacharya, S. (2024) · IEEE SoutheastCon · doi:10.1109/southeastcon52093.2024.10500274
Comment on Jakob, Dechamps & Maier
Bancel, P. A. (2024) · Journal of Anomalous Experience and Cognition · doi:10.31156/jaex.25420
Sigurðardottir, J. N., & Ayis, S. A. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009201704.027
Data presentation for urology research
Zhang, A., Bajic, P., & Lundy, S. D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-90186-4.00053-5
Selective opioid-sparing effects of cannabidiol on opioid analgesia in rats
Brice‐Tutt, A. C., Murphy, N., Setlow, B., Senetra, A. S., Gour, A., Serres, T., Malphurs, W. L., Behnood‐Rod, A., Kramer, C. M., Caudle, R., et al. (2026) · Journal of Pain · doi:10.1016/j.jpain.2026.106338
Boland, M. R. (2024) · Springer eBooks · doi:10.1007/978-3-031-74383-2_15
Determining Phenotypic Traits from Single Nucleotide Polymorphism (SNP) Data
Boland, M. R. (2024) · Springer eBooks · doi:10.1007/978-3-031-74383-2_3
Arzounian, D., Guérit, F., Deeks, J. M., Garcia, C., Groote, E. D., Bance, M., & Carlyon, R. P. (2025) · Hearing Research · doi:10.1016/j.heares.2025.109338
Nasal airway resistance in moderately severe allergic rhinitis and impact of Vitamin D deficiency
Sunar, D., Kajal, S., Pachaury, S. S., Gupta, Y., Thakar, A., Sikka, K., & Verma, H. (2025) · Indian Journal of Allergy Asthma and Immunology · doi:10.4103/ijaai.ijaai_51_24
Effective Statistics Are Essential for Strengthening a Paper in Brain and Behavior
Ordak, M., & Bosurgi, R. (2025) · Brain and Behavior · doi:10.1002/brb3.70727
Ortega‐Paz, L., Talasaz, A. H., Sadeghipour, P., Rashedi, S., Connors, J. M., Angiolillo, D. J., Cavallari, L. H., Jiménez, D., Bastidas, G., Lorenzi, E., et al. (2025) · Thrombosis Research · doi:10.1016/j.thromres.2025.109484 · Cited by 1
A comprehensive meta-analysis of workplace friendship: a resource-based perspective
Chen, Y., Yang, C., Chen, A., & Yang, X. (2025) · European Journal of Work and Organizational Psychology · doi:10.1080/1359432x.2025.2557315
Importancia de la Estadística en los Procesos de Investigación Universitaria
Alvarez, M. D. L. A. M. (2025) · Educational Regent Multidisciplinary Journal · doi:10.63969/rgtt7645
Longaretti, Y., Cheron, G. A., & Zarka, D. (2025) · The Journal of Psychology · doi:10.1080/00223980.2025.2575309
Balasubramaniam, A., Reidsma, D., & Heylen, D. (2025) · Annual Conference on Human-Computer Interaction and Sports (SportsHCI) · doi:10.1145/3749385.3749395
Editorial: The most common mistake involving the use of correlations
McCormick, C. M., & Carré, J. M. (2025) · Hormones and Behavior · doi:10.1016/j.yhbeh.2025.105848
Sex as a biological variable and the pursuitworthiness of exploratory inquiry
DiMarco, M. (2025) · European Journal for Philosophy of Science · doi:10.1007/s13194-025-00701-0
Gender Perspectives in Sensory and Consumer Science
Spinelli, S., & Toschi, T. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29139-5.00064-1
Eye tracking during conventional neuropsychological assessments of spatial neglect
Vittersø, A. D., Elshout, J. A., der Stigchel, S. V., & Nijboer, T. C. W. (2025) · Applied Neuropsychology Adult · doi:10.1080/23279095.2025.2605302
Gompers, A. D., Olivier, M. T., & Maney, D. L. (2024) · Figshare · doi:10.6084/m9.figshare.26701696
Gompers, A. D., Olivier, M. T., & Maney, D. L. (2024) · Figshare · doi:10.6084/m9.figshare.26701708
Gompers, A. D., Olivier, M. T., & Maney, D. L. (2024) · Figshare · doi:10.6084/m9.figshare.26701702
Gompers, A. D., Olivier, M. T., & Maney, D. L. (2024) · Figshare · doi:10.6084/m9.figshare.26701705
Gompers, A. D., Olivier, M. T., & Maney, D. L. (2024) · Figshare · doi:10.6084/m9.figshare.26701699
Gompers, A. D., Olivier, M. T., & Maney, D. L. (2024) · Figshare · doi:10.6084/m9.figshare.26701711
Lynch, C., Baudier, K. M., Montgomery, D., & Barrett, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.06.692245 · Cited by 1
Assessing chance in neuro-vascular interactions
Aksenov, D. P., Doubovikov, E. D., Serdyukova, N. A., Gascoigne, D. A., Miller, M. J., Drobyshevsky, A., & Linsenmeier, R. A. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121676
Guisnet, A., Halaby, N., Rivest, M., Quineche, B. R., & Hendricks, M. (2026) · Biology Open · doi:10.1242/bio.062282
Olivier, M. T., Brown, A. W., Chung, S., Vorland, C. J., & Maney, D. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.04.703900
Exploring of the the neural mechanisms of fatigue by passive induction
Ioannucci, S. (2022) · theses.fr (ABES) · doi:10.70675/dae7d651z6aefz4170z8365z14422797e54f
Sirparanta, A. (2024) · theses.fr (ABES) · doi:10.70675/01e34829zd16az44abz83d6z94398e631330
Arslanova, I., Jaglinska, M., & Manos Tsakiris, A. (2026) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2026.109550
The physicochemical basis of protein evolution: property-informed evolutionary models (PRIME)
Kim, H., Scheffler, K., Nekrutenko, A., Martin, D. P., Weaver, S., Murrell, B., & Pond, S. L. K. (2026) · Molecular Biology and Evolution · doi:10.1093/molbev/msag200
Fali, I. M., & Nnenna, E. C. (2026) · Qubahan Journal of Coaching and Sports Sciences · doi:10.48161/qjcs.v4n4a90
Hewitt, K., & Huang, X.-F. (2026) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2026.110940
Metadata
- 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 › Library and Information Sciences › 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
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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.
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