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SIMPLICITY AND COMPLEXITY IN ECOLOGICAL DATA ANALYSIS
Author(s) -
Murtaugh Paul A.
Publication year - 2007
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/0012-9658(2007)88[56:sacied]2.0.co;2
Subject(s) - simplicity , key (lock) , simple (philosophy) , computer science , focus (optics) , ecology , observational study , data science , management science , epistemology , biology , mathematics , economics , statistics , philosophy , physics , optics
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.