Whole-Ecosystem Experiments: Replication and Arguing from Error
Whole-Ecosystem Experiments: Replication and Arguing from Error
Deborah Mayo (1996) has reinterpreted classic frequentist statistics into a much more general framework that she calls error statistics to indicate the continuing centrality and importance of error probabilities and error-probabilistic reasoning in testing hypotheses. Her generalization of statistical reasoning above and beyond any one statistical test provides a consistent and coherent approach to testing and assessing both quantitative and qualitative evidence and hence can be directly applied to whole-ecosystem experiments. This chapter argues that understanding the types of errors that replication controls allows for better design and interpretation of unreplicated and semi-replicated whole-ecosystem experiments. It begins by clarifying the meaning of three common concepts used in debates about what can and cannot be learned from whole-ecosystem manipulations: replication, BACI design, and pseudoreplication. It then rephrases Stuart Hurlbert's first error of concern and discusses replication as a check on stochastic events beyond natural variation.
Keywords: Deborah Mayo, error statistics, replication, whole-ecosystem experiments, BACI design, pseudoreplication, Stuart Hurlbert, stochastic events, statistical reasoning, errors
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