Western EcoSystems Technology, Inc., 1610 East Reynolds Street, Laramie, Wyoming, 82072, USA.
Department of Statistics and Data Science, Cornell University, Ithaca, New York, 14853, USA.
Ecology. 2021 Jun;102(6):e03336. doi: 10.1002/ecy.3336. Epub 2021 May 4.
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.
在竞争激烈的统计模型中进行选择是科学的核心挑战。然而，模型选择的许多可能方法和技术，以及对其使用的相互矛盾的建议，可能会令人困惑。我们认为，围绕统计模型选择的许多混淆源于未能首先明确分析的目的。我们认为，生态学中统计建模有三个不同的目标：数据探索、推理和预测。一旦明确了建模目标，就更容易确定合适的模型选择过程。我们回顾了模型选择方法，并强调了它们相对于三个建模目标中的每一个的优缺点。然后，我们使用蝴蝶种群计数的时间序列提供了探索、推理和预测建模的示例。这些例子展示了模型选择方法如何自然地从建模目标中产生，从而导致针对不同目的选择不同的模型，即使使用完全相同的数据集也是如此。本综述说明了生态学家的最佳实践，应该提醒人们，统计方法不能替代批判性思维，也不能替代使用独立数据来检验假设和验证预测。
