Pseudoreplication and the Design of Ecological Field Experiments
1984/06/01 by Stuart H. Hurlbert · 8,350 citations
Agricultural and Biological Sciences · Environmental Science · Mathematics · #Biology #Computer science #Ecology #Ecology and Vegetation Dynamics Studies #Mathematics #Plant and animal studies #Species Distribution and Climate Change #Statistical hypothesis testing #Statistics #Terminology #Type I and type II errors
paper · doi:10.2307/1942661
published in Ecological Monographs 54(2), 187-211 (Wiley)
openalex publication_date 1984/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract
Pseudoreplication is defined as the use of inferential statistics to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent. In ANOVA terminology, it is the testing for treatment effects with an error term inappropriate to the hypothesis being considered. Scrutiny of 176 experimental studies published between 1960 and the present revealed that pseudoreplication occurred in 27% of them, or 48% of all such studies that applied inferential statistics. The incidence of pseudoreplication is especially high in studies of marine benthos and small mammals. The critical features of controlled experimentation are reviewed. Nondemonic intrusion is defined as the impingement of chance events on an experiment in progress. As a safeguard against both it and preexisting gradients, interspersion of treatments is argued to be an obligatory feature of good design. Especially in small experiments, adequate interspersion can sometimes be assured only by dispensing with strict randomization procedures. Comprehension of this conflict between interspersion and randomization is aided by distinguishing pre—layout (or conventional) and layout—specific alpha (probability of type I error). Suggestions are offered to statisticians and editors of ecological journals as to how ecologists' understanding of experimental design and statistics might be improved.
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