A practical guide to selecting models for exploration, inference, and prediction in ecology

Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results f...

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Vydáno v:Ecology (Durham) Ročník 102; číslo 6; s. 1 - 16
Hlavní autoři: Tredennick, Andrew T., Hooker, Giles, Ellner, Stephen P., Adler, Peter B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States John Wiley and Sons, Inc 01.06.2021
Ecological Society of America
John Wiley and Sons Inc
Témata:
Best practice
butterflies
Concepts & Synthesis
CONCEPTS & SYNTHESIS: EMPHASIZING NEW IDEAS TO STIMULATE RESEARCH IN ECOLOGY
data collection
Ecology
Exploration
Mathematical models
model selection
prediction
Predictions
Statistical analysis
Statistical inference
Statistical models
time series analysis
validation
variable selection
model selection
prediction
variable selection
validation
ISSN:0012-9658, 1939-9170, 1939-9170
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Popis
Shrnutí:Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions.
Bibliografie:Corresponding Editor: Derek M. Johnson.
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ISSN:0012-9658
1939-9170
1939-9170
DOI:10.1002/ecy.3336