The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Pseudoreplication Revisited
Open accessAbstract
during 1960-1980 for evidence of pseudoreplication (Hurlbert 1984).Pseudoreplication is defined by Hurlbert (1984: 18) as
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The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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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 [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 [5]
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Citations
Cites
Pseudoreplication and the Design of Ecological Field Experiments
Hurlbert, S. H. (1984) · Ecological Monographs · doi:10.2307/1942661 · Cited by 8,377
Statistical Power Analysis can Improve Fisheries Research and Management
Peterman, R. M. (1990) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/f90-001 · Cited by 788
Distribution‐Free and Robust Statistical Methods: Viable Alternatives to Parametric Statistics
Potvin, C., & Roff, D. A. (1993) · Ecology · doi:10.2307/1939920 · Cited by 464
Detecting Community-Wide Patterns: Estimating Power Strengthens Statistical Inference
Toft, C. A., & Shea, P. J. (1983) · The American Naturalist · doi:10.1086/284162 · Cited by 269
Large‐Scale Perturbations: Opportunities for Innovation
Carpenter, S. R. (1990) · Ecology · doi:10.2307/1938617 · Cited by 195
Detecting Changes in Ecological Time Series
Jassby, A. D., & Powell, T. M. (1990) · Ecology · doi:10.2307/1938618 · Cited by 141
Bayesian Inference in Non‐Replicated Ecological Studies
Reckhow, K. H. (1990) · Ecology · doi:10.2307/1938619 · Cited by 104
Pseudo‐Understanding of Pseudoreplication: A Cautionary Note
(1986) · Bulletin of the Ecological Society of America · doi:10.2307/20166513 · Cited by 46
Concluding Remarks: A Drop in the Ocean
Potvin, C., & Travis, J. (1993) · Ecology · doi:10.2307/1939925 · Cited by 6
Flagged first, then by citations
Research works printed but not linked: 3
- Crowley, P. H. 1992. Resampling methods for computation- intensive data analyses in ecology and evolution. Annual Review Ecology and Systematics 23:405-447.
- Hurlbert, S. H., and M. D. White. 1993. Experiments with freshwater invertebrate zooplanktivores: quality of statis- tical analysis. Bulletin of Marine Science 53:128-153.
- Innis, G. S. 1979. Letter to the editor. Bulletin of the Eco- logical Society of America 60:142.
Cited by
Sorted by citations
Predictive habitat distribution models in ecology
Guisan, A., & Zimmermann, N. E. (2000) · Ecological Modelling · doi:10.1016/s0304-3800(00)00354-9 · Cited by 7,359
Ecological models and data in R
Bolker, B. M. (2008) · doi:10.2307/j.ctvcm4g37 · Cited by 1,297
“The basics of experimental design — the meaning of and need for randomization, control, independence, and replication in setting up experiments, the idea of statistical power, and the concept of pseudoreplication (Hurlbert, 1984; Hargrove and Pickering, 1992; Heffner et al., 1996; Oksanen, 2001) — are essential tools…”
BackgroundGrazing vs. mowing: A meta-analysis of biodiversity benefits for grassland management
Tälle, M., Déak, B., Poschlod, P., Valkó, O., Westerberg, L. M., & Milberg, P. (2016) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2016.02.008 · Cited by 420
The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis?
Lazic, S. E. (2010) · BMC Neuroscience · doi:10.1186/1471-2202-11-5 · Cited by 381
A practical solution to pseudoreplication bias in single-cell studies
Zimmerman, K. D., Espeland, M. A., & Langefeld, C. D. (2021) · Nature Communications · doi:10.1038/s41467-021-21038-1 · Cited by 356
Metadata
- Authors
- Heffner, R. A., Butler, M. J., & Reilly, C. K.
- Venue
- Ecology
- Publisher
- Wiley
- Published
- 1996-12
- Volume
- 77
- Issue
- 8
- Pages
- 2558-2562
- ISSN
- 0012-9658 (ISSN-L)
- 1939-9170
- OpenAlex
- W4240529425
- Semantic Scholar
- 79be96546a85de95d191acaf9aaaf35799fb84c4
- Cited by
- 139
- References
- 9
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 6 · 2023: 6 · 2022: 11 · 2021: 4 · 2020: 5 · 2019: 3 · 2018: 4 · 2017: 7 · 2016: 7 · 2015: 5 · 2014: 4 · 2013: 3 · 2012: 6
- FWCI
- 2.44
- Type
- journal article
- Language
- en
- Open access
- Locations
- Ecology (publishedVersion)
- ODU Digital Commons (Old Dominion University) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- pseudoreplication, statistical inference, repeated measures, sample size, statistical power, ecological data analysis
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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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