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.
Keywords
Related Publications
Design and Analysis of Ecological Experiments
Contributors 1: Samuel M. Scheiner: Theories, Hypotheses, and Statistics 2: Robert J. Steidl and Len Thomas: Power Analysis and Experimental Design 3: Aaron M. Ellison: Explorat...
FITTING MULTIVARIATE MODELS TO COMMUNITY DATA: A COMMENT ON DISTANCE-BASED REDUNDANCY ANALYSIS
Nonparametric multivariate analysis of ecological data using permutation tests has two main challenges: (1) to partition the variability in the data according to a complex desig...
The Simes Method for Multiple Hypothesis Testing With Positively Dependent Test Statistics
Abstract The Simes method for testing intersection of more than two hypotheses is known to control the probability of type I error only when the underlying test statistics are i...
Statistical methods of estimation and inference for functional MR image analysis
Abstract Two questions arising In the analysis of functional magnetic resonance imaging (fMRI) data acquired during periodic sensory stimulation are: i) how to measure the exper...
Significance, Errors, Power, and Sample Size: The Blocking and Tackling of Statistics
Inferential statistics relies heavily on the central limit theorem and the related law of large numbers. According to the central limit theorem, regardless of the distribution o...
Publication Info
- Year
- 1984
- Type
- article
- Volume
- 54
- Issue
- 2
- Pages
- 187-211
- Citations
- 8162
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.2307/1942661