Resampling-based methods for biologists.
Bootstrap
Model uncertainty
Permutation
Randomization
Replication
Resampling
Statistical inference
Journal
PeerJ
ISSN: 2167-8359
Titre abrégé: PeerJ
Pays: United States
ID NLM: 101603425
Informations de publication
Date de publication:
2020
2020
Historique:
received:
23
10
2019
accepted:
08
04
2020
entrez:
19
5
2020
pubmed:
19
5
2020
medline:
19
5
2020
Statut:
epublish
Résumé
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,
Identifiants
pubmed: 32419987
doi: 10.7717/peerj.9089
pii: 9089
pmc: PMC7211410
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e9089Informations de copyright
© 2020 Fieberg et al.
Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
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