Combining statistical inference and decisions in ecology

Ecological Applications
By:  and 



Statistical decision theory (SDT) is a sub-field of decision theory that formally incorporates statistical investigation into a decision-theoretic framework to account for uncertainties in a decision problem. SDT provides a unifying analysis of three types of information: statistical results from a data set, knowledge of the consequences of potential choices (i.e., loss), and prior beliefs about a system. SDT links the theoretical development of a large body of statistical methods including point estimation, hypothesis testing, and confidence interval estimation. The theory and application of SDT have mainly been developed and published in the fields of mathematics, statistics, operations research, and other decision sciences, but have had limited exposure in ecology. Thus, we provide an introduction to SDT for ecologists and describe its utility for linking the conventionally separate tasks of statistical investigation and decision making in a single framework. We describe the basic framework of both Bayesian and frequentist SDT, its traditional use in statistics, and discuss its application to decision problems that occur in ecology. We demonstrate SDT with two types of decisions: Bayesian point estimation, and an applied management problem of selecting a prescribed fire rotation for managing a grassland bird species. Central to SDT, and decision theory in general, are loss functions. Thus, we also provide basic guidance and references for constructing loss functions for an SDT problem.

Publication type Article
Publication Subtype Journal Article
Title Combining statistical inference and decisions in ecology
Series title Ecological Applications
DOI 10.1890/15-1593.1
Volume 26
Issue 6
Year Published 2016
Language English
Publisher Ecological Society of America
Contributing office(s) Coop Res Unit Seattle
Description 13 p.
First page 1930
Last page 1942
Online Only (Y/N) N
Additional Online Files (Y/N) N
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