In the deep end: pooling data and other statistical challenges of zoo and aquarium research
Article first published online: 21 APR 2006
© 2006 Wiley-Liss, Inc.
Volume 25, Issue 4, pages 339–352, July/August 2006
How to Cite
Kuhar, C. W. (2006), In the deep end: pooling data and other statistical challenges of zoo and aquarium research. Zoo Biol., 25: 339–352. doi: 10.1002/zoo.20089
- Issue published online: 10 AUG 2006
- Article first published online: 21 APR 2006
- Manuscript Accepted: 27 DEC 2005
- Manuscript Received: 7 OCT 2005
- research design;
- captive research;
Zoo and aquarium research presents many logistic challenges, including extremely small sample sizes and lack of independent data points, which lend themselves to the misuse of statistics. Pseudoreplication and pooling of data are two statistical problems common in research in the biological sciences. Although the prevalence of these and other statistical miscues have been documented in other fields, little attention has been paid to the practice of statistics in the field of zoo biology. A review of articles published in the journal Zoo Biology between 1999–2004 showed that approximately 40% of the 146 articles utilizing inferential statistics during that span contained some evidence of pseudoreplication or pooling of data. Nearly 75% of studies did not provide degrees of freedom for all statistics and approximately 20% did not report test statistic values. Although the level of pseudoreplication in this dataset is not outside the levels found in other branches of biology, it does indicate the challenges of dealing with appropriate data analysis in zoo and aquarium studies. The standardization of statistical techniques to deal with the methodological challenges of zoo and aquarium populations can help advance zoo research by guiding the production and analysis of applied studies. This study recommends techniques for dealing with these issues, including complete disclosure of data manipulation and reporting of statistical values, checking and control for institutional effects in statistical models, and avoidance of pseudoreplicated observations. Additionally, zoo biologists should seek out other models such as hierarchical or factorial models or randomization tests to supplement their repertoire of t-tests and ANOVA. These suggestions are intended to stimulate conversation and examination of the current use of statistics in zoo biology in an effort to develop more consistent requirements for publication. Zoo Biol 0:1–14, 2006. © 2006 Wiley-Liss, Inc.