Whole-Ecosystem Experiments: Replication and Arguing from Error

Jean A. Miller and Thomas M. Frost

in The Nature of Scientific Evidence

Published by University of Chicago Press

Published in print October 2004 | ISBN: 9780226789552
Published online February 2013 | e-ISBN: 9780226789583 | DOI:
Whole-Ecosystem Experiments: Replication and Arguing from Error

More Like This

Show all results sharing this subject:

  • Animal Pathology and Diseases


Show Summary Details


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

Chapter.  21295 words.  Illustrated.

Subjects: Animal Pathology and Diseases

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.