Fieberg John R, Vitense Kelsey, Johnson Douglas H
Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, USA.
PeerJ. 2020 May 7;8:e9089. doi: 10.7717/peerj.9089. eCollection 2020.
Ecological data often violate common assumptions of traditional parametric statistics (e.g., that residuals are Normally distributed, have constant variance, and cases are independent). Modern statistical methods are well equipped to handle these complications, but they can be challenging for non-statisticians to understand and implement. Rather than default to increasingly complex statistical methods, resampling-based methods can sometimes provide an alternative method for performing statistical inference, while also facilitating a deeper understanding of foundational concepts in frequentist statistics (e.g., sampling distributions, confidence intervals, -values). Using simple examples and case studies, we demonstrate how resampling-based methods can help elucidate core statistical concepts and provide alternative methods for tackling challenging problems across a broad range of ecological applications.
生态数据常常违背传统参数统计的常见假设（例如，残差呈正态分布、具有恒定方差且样本相互独立）。现代统计方法有能力处理这些复杂情况，但对于非统计学家而言，理解和应用它们可能具有挑战性。与其默认采用日益复杂的统计方法，基于重采样的方法有时可以提供一种进行统计推断的替代方法，同时还能促进对频率主义统计学基础概念（例如抽样分布、置信区间、P值）的更深入理解。通过简单的示例和案例研究，我们展示了基于重采样的方法如何有助于阐明核心统计概念，并为解决广泛生态应用中的挑战性问题提供替代方法。
