title: Pseudoreplication conventions are testable hypotheses.
abstract: Hurlbert's conceptions of pseudoreplication, such as the loss of independent replicates with repeated sampling over time and the lack of appropriate spatial interspersion of experimental units to achieve statistical independence, are really theoretical hypotheses that warrant empirical confirmation or disconfirmation. Schank and Koehnle have provided a valuable service to researchers in ecology, conservation biology, and animal behavior using logical argument and repeated Monte Carlo simulations to challenge Hurlbert's theoretical assumptions about statistical independence. Their simulations showed that averaging samples to produce a single datum destroys the information within experimental units and is mathematically equivalent to the treatment level in a nested analysis of variance. Such results counter Hurlbert's argument that pooling is wrong. Rigid thinking about sampling, as engendered by Hurlbert's pseudoreplication arguments, does not promote the exploration of new methodologies and there is something disturbingly inconsistent with the scientific method when journal editors and reviewers adopt Hurlbert's pseudoreplication arguments unquestionably.
authors.affiliation: Department of Psychology, University of California, Davis, CA 95616, USA. rgcoss@ucdavis.edu
authors.forename: Richard G
authors.initials: RG
authors.lastname: Coss
mesh_terms: D000704:Analysis of Variance; D000818:Animals; D001522:Behavior, Animal; D015982:Bias; D016000:Cluster Analysis; D004463:Ecology; D035081:Editorial Policies; D006801:Humans; D033183:Interdisciplinary Communication; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D009010:Monte Carlo Method; D011643:Publishing; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012107:Research Design / Q000706:statistics & numerical data*; D012494:Sampling Studies