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Cited by 381Statistical Methods and Bayesian InferenceMeta-analysis and systematic reviewsStatistical Methods in Epidemiology

The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.

The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis?

Open access
Lazic, Stanley E·2010·BMC Neuroscience
DOI 10.1186/1471-2202-11-5PMC PMC2817684
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Abstract

BACKGROUND: Pseudoreplication occurs when observations are not statistically independent, but treated as if they are. This can occur when there are multiple observations on the same subjects, when samples are nested or hierarchically organised, or when measurements are correlated in time or space. Analysis of such data without taking these dependencies into account can lead to meaningless results, and examples can easily be found in the neuroscience literature. RESULTS: A single issue of Nature Neuroscience provided a number of examples and is used as a case study to highlight how pseudoreplication arises in neuroscientific studies, why the analyses in these papers are incorrect, and appropriate analytical methods are provided. 12% of papers had pseudoreplication and a further 36% were suspected of having pseudoreplication, but it was not possible to determine for certain because insufficient information was provided. CONCLUSIONS: Pseudoreplication can undermine the conclusions of a statistical analysis, and it would be easier to detect if the sample size, degrees of freedom, the test statistic, and precise p-values are reported. This information should be a requirement for all publications.

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Figure 1 An example of pseudoreplication. Two rats are sampled from a population with a mean (μ) of 50 and a standard deviation (σ) of 10, and ten measurements of an arbitrary outcome variable are made on each rat. The first (incorrect) 90% CI uses all 20 data points and does not account for the hierarchical nature of the data. For the second 90% CI, the mean of the ten values for each rat are calculated first, and then only these two averaged values are used for the calculation of the CI. The error bar on the left is incorrect because each of the 20 data points are not a random sample from the whole population, but rather samples within two rats. This is evident from the fact that the 10 points are normally distributed around the mean of their respective rats, but not normally distributed around the population mean (horizontal grey line), as would be expected when independent samples are randomly drawn from a population. Increasing the number of observations on each rat does not lead to a more precise estimate of μ, which requires more rats. Note that 90% CI are plotted for clarity because the graph needs to be greatly compressed to display the 95% CI.
Figure 1 An example of pseudoreplication. Two rats are sampled from a population with a mean (μ) of 50 and a standard deviation (σ) of 10, and ten measurements of an arbitrary outcome variable are made on each rat. The first (incorrect) 90% CI uses all 20 data points and does not account for the hierarchical nature of the data. For the second 90% CI, the mean of the ten values for each rat are calculated first, and then only these two averaged values are used for the calculation of the CI. The error bar on the left is incorrect because each of the 20 data points are not a random sample from the whole population, but rather samples within two rats. This is evident from the fact that the 10 points are normally distributed around the mean of their respective rats, but not normally distributed around the population mean (horizontal grey line), as would be expected when independent samples are randomly drawn from a population. Increasing the number of observations on each rat does not lead to a more precise estimate of μ, which requires more rats. Note that 90% CI are plotted for clarity because the graph needs to be greatly compressed to display the 95% CI.CC BY · PubMed Central

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Metadata

Authors
Lazic, S. E.
Affiliations
F. Hoffmann-La Roche AG (CH)
Corresponding author
Lazic, Stanley E
ORCID
Lazic, Stanley E
Venue
BMC Neuroscience
Publisher
Springer Science and Business Media LLC
Published
2010-01-14
Volume
11
Issue
1
Pages
5-5
ISSN
1471-2202 (ISSN-L)
DOI
10.1186/1471-2202-11-5
PMC
PMC2817684
OpenAlex
W2109626712
Cited by
381
References
90
Citations by year
2026: 68 · 2025: 30 · 2024: 19 · 2023: 32 · 2022: 20 · 2021: 21 · 2020: 31 · 2019: 16 · 2018: 23 · 2017: 22 · 2016: 15 · 2015: 19 · 2014: 20 · 2013: 17 · 2012: 19
FWCI
12.28
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Neuroscience (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • KAIST Institutional Repository (KAIST) (submittedVersion)
  • King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion, other-oa)
Funders
  • Cancer Research UK (10218)
  • University of Oxford
  • Cambridge Commonwealth Trust
  • King Abdullah University of Science and Technology (KUK‐C1‐013‐04)
  • Korea Science and Engineering Foundation
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords
pseudoreplication, statistical dependence, nested data, hierarchical data, repeated measures, time series correlation, spatial correlation, statistical analysis errors, sample size reporting, degrees of freedom, p-values
MeSH terms
Analysis of Variance, Animals, Computer Simulation, Neurosciences, Periodicals as Topic, Time Factors, Models, Statistical, Linear Models, Biomedical Research

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