Resampling-based methods for biologists

John R. Fieberg, Kelsey Vitense, Douglas H. Johnson

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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, p-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.

Original languageEnglish (US)
Article number9089
JournalPeerJ
Volume2020
Issue number3
DOIs
StatePublished - 2020

Bibliographical note

Funding Information:
John R. Fieberg received partial salary support from the Minnesota Agricultural Experimental Station and the McKnight Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2020 Fieberg et al.

Keywords

  • Bootstrap
  • Model uncertainty
  • Permutation
  • Randomization
  • Replication
  • Resampling
  • Statistical inference

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