# Combining statistical inference and decisions in ecology

Overview of attention for article published in Ecological Applications, September 2016

• In the top 25% of all research outputs scored by Altmetric
• High Attention Score compared to outputs of the same age (86th percentile)
• Good Attention Score compared to outputs of the same age and source (76th percentile)

blogs
1 blog
11 X users

## Citations

dimensions_citation
71 Dimensions

mendeley
121 Mendeley
Title Combining statistical inference and decisions in ecology Ecological Applications, September 2016 10.1890/15-1593.1 Perry J. Williams, Mevin B. Hooten 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.
X Demographics

## X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
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The data shown below were compiled from readership statistics for 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.

### Geographical breakdown

Country Count As %
United States 6 5%
Japan 1 <1%
Spain 1 <1%
France 1 <1%
Unknown 112 93%

### Demographic breakdown

Readers by professional status Count As %
Researcher 30 25%
Student > Ph. D. Student 27 22%
Student > Master 13 11%
Other 9 7%
Professor 5 4%
Other 19 16%
Unknown 18 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 33%
Environmental Science 30 25%
Earth and Planetary Sciences 5 4%
Mathematics 3 2%
Business, Management and Accounting 3 2%
Other 12 10%
Unknown 28 23%
Attention Score in Context

## Attention Score in Context

This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 January 2021.
All research outputs
#2,854,563
of 26,012,510 outputs
Outputs from Ecological Applications
#749
of 3,377 outputs
Outputs of similar age
#46,720
of 350,172 outputs
Outputs of similar age from Ecological Applications
#14
of 60 outputs
Altmetric has tracked 26,012,510 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,377 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.7. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 350,172 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.