title: Simplicity and complexity in ecological data analysis.
abstract: I argue that ecological data analyses are often needlessly complicated, and I present two examples of published analyses for which simpler alternatives are available. Unnecessary complexity is often introduced when analysts focus on subunits of the key experimental or observational units in a study, or use a very general framework to present an analysis that is a simple special case. Simpler analyses are easier to explain and understand; they clarify what the key units in a study are; they reduce the chances for computational mistakes; and they are more likely to lead to the same conclusions when applied by different analysts to the same data.
authors.affiliation: Department of Statistics, Oregon State University, Corvallis, Oregon 97331, USA. murtaugh@science.oregonstate.edu
authors.forename: Paul A
authors.initials: PA
authors.lastname: Murtaugh
mesh_terms: D000704:Analysis of Variance; D000818:Animals; D003531:Cypriniformes; D003627:Data Interpretation, Statistical; D004463:Ecology / Q000379:methods*; D012107:Research Design; D015048:Zooplankton