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

Handle URI:
http://hdl.handle.net/10754/596825
Title:
The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis?
Authors:
Lazic, Stanley E
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.
Citation:
Lazic SE (2010) The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? BMC Neurosci 11: 5. Available: http://dx.doi.org/10.1186/1471-2202-11-5.
Publisher:
Springer Nature
Journal:
BMC Neuroscience
KAUST Grant Number:
KUK-C1-013-04
Issue Date:
14-Jan-2010
DOI:
10.1186/1471-2202-11-5
PubMed ID:
20074371
PubMed Central ID:
PMC2817684
Type:
Article
ISSN:
1471-2202
Sponsors:
SEL was supported by a Cancer Research UK bursary, the Cambridge Commonwealth Trust (University of Cambridge), and an OCCAM Visiting Studentship (University of Oxford). This publication was based on work supported in part by Award No KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST). The helpful comments and suggestions from four anonymous reviewers is also gratefully acknowledged.
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Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorLazic, Stanley Een
dc.date.accessioned2016-02-21T08:51:23Zen
dc.date.available2016-02-21T08:51:23Zen
dc.date.issued2010-01-14en
dc.identifier.citationLazic SE (2010) The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? BMC Neurosci 11: 5. Available: http://dx.doi.org/10.1186/1471-2202-11-5.en
dc.identifier.issn1471-2202en
dc.identifier.pmid20074371en
dc.identifier.doi10.1186/1471-2202-11-5en
dc.identifier.urihttp://hdl.handle.net/10754/596825en
dc.description.abstractBACKGROUND: 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.en
dc.description.sponsorshipSEL was supported by a Cancer Research UK bursary, the Cambridge Commonwealth Trust (University of Cambridge), and an OCCAM Visiting Studentship (University of Oxford). This publication was based on work supported in part by Award No KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST). The helpful comments and suggestions from four anonymous reviewers is also gratefully acknowledged.en
dc.publisherSpringer Natureen
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en
dc.subject.meshModels, Statisticalen
dc.titleThe problem of pseudoreplication in neuroscientific studies: is it affecting your analysis?en
dc.typeArticleen
dc.identifier.journalBMC Neuroscienceen
dc.identifier.pmcidPMC2817684en
dc.contributor.institutionUniversity of Cambridge, Cambridge, United Kingdomen
dc.contributor.institutionUniversity of Oxford, Oxford, United Kingdomen
dc.contributor.institutionF. Hoffmann-La Roche AG, Basel, Switzerlanden
kaust.grant.numberKUK-C1-013-04en

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