Four principles for improved statistical ecology

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUNSW Sydney-
Autor(es): dc.contributorthe Environment and Water-
Autor(es): dc.contributorJagiellonian University-
Autor(es): dc.contributorUniversity of St Andrews-
Autor(es): dc.contributorFaculdade de Ciências da Universidade de Lisboa-
Autor(es): dc.contributorThe Analytical Edge Statistical Consulting-
Autor(es): dc.contributorGlobal Fishing Watch-
Autor(es): dc.contributorUniversity of Cape Town-
Autor(es): dc.contributorThe University of Melbourne-
Autor(es): dc.contributorVictoria University of Wellington-
Autor(es): dc.contributorUniversity of Massachusetts at Amherst-
Autor(es): dc.contributorUniversity of Rhode Island-
Autor(es): dc.contributorVertebrate Pest Research Unit-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Bayreuth-
Autor(es): dc.contributorUniversity of Regensburg-
Autor(es): dc.contributorUniversity of Alberta-
Autor(es): dc.creatorPopovic, Gordana-
Autor(es): dc.creatorMason, Tanya Jane-
Autor(es): dc.creatorDrobniak, Szymon Marian-
Autor(es): dc.creatorMarques, Tiago André-
Autor(es): dc.creatorPotts, Joanne-
Autor(es): dc.creatorJoo, Rocío-
Autor(es): dc.creatorAltwegg, Res-
Autor(es): dc.creatorBurns, Carolyn Claire Isabelle-
Autor(es): dc.creatorMcCarthy, Michael Andrew-
Autor(es): dc.creatorJohnston, Alison-
Autor(es): dc.creatorNakagawa, Shinichi-
Autor(es): dc.creatorMcMillan, Louise-
Autor(es): dc.creatorDevarajan, Kadambari-
Autor(es): dc.creatorTaggart, Patrick Leo-
Autor(es): dc.creatorWunderlich, Alison-
Autor(es): dc.creatorMair, Magdalena M.-
Autor(es): dc.creatorMartínez-Lanfranco, Juan Andrés-
Autor(es): dc.creatorLagisz, Malgorzata-
Autor(es): dc.creatorPottier, Patrice-
Data de aceite: dc.date.accessioned2025-08-21T18:21:09Z-
Data de disponibilização: dc.date.available2025-08-21T18:21:09Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1111/2041-210X.14270-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309964-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309964-
Descrição: dc.descriptionIncreasing attention has been drawn to the misuse of statistical methods over recent years, with particular concern about the prevalence of practices such as poor experimental design, cherry picking and inadequate reporting. These failures are largely unintentional and no more common in ecology than in other scientific disciplines, with many of them easily remedied given the right guidance. Originating from a discussion at the 2020 International Statistical Ecology Conference, we show how ecologists can build their research following four guiding principles for impactful statistical research practices: (1) define a focussed research question, then plan sampling and analysis to answer it; (2) develop a model that accounts for the distribution and dependence of your data; (3) emphasise effect sizes to replace statistical significance with ecological relevance; and (4) report your methods and findings in sufficient detail so that your research is valid and reproducible. These principles provide a framework for experimental design and reporting that guards against unsound practices. Starting with a well-defined research question allows researchers to create an efficient study to answer it, and guards against poor research practices that lead to poor estimation of the direction, magnitude, and uncertainty of ecological relationships, and to poor replicability. Correct and appropriate statistical models give sound conclusions. Good reporting practices and a focus on ecological relevance make results impactful and replicable. Illustrated with two examples—an experiment to study the impact of disturbance on upland wetlands, and an observational study on blue tit colouring—this paper explains the rationale for the selection and use of effective statistical practices and provides practical guidance for ecologists seeking to improve their use of statistical methods.-
Descrição: dc.descriptionNational Research Foundation-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionStats Central Mark Wainwright Analytical Centre UNSW Sydney-
Descrição: dc.descriptionCentre for Ecosystem Science School of Biological Earth and Environmental Sciences UNSW Sydney-
Descrição: dc.descriptionScience Economics and Insights Division NSW Department of Climate Change Energy the Environment and Water-
Descrição: dc.descriptionEvolution and Ecology Research Centre School of Biological Earth and Environmental Sciences UNSW Sydney-
Descrição: dc.descriptionInstitute of Environmental Sciences Jagiellonian University-
Descrição: dc.descriptionCentre for Research into Ecological and Environmental Modelling The Observatory University of St Andrews-
Descrição: dc.descriptionCentro de Estatística e Aplicações Departamento de Biologia Animal Faculdade de Ciências da Universidade de Lisboa-
Descrição: dc.descriptionThe Analytical Edge Statistical Consulting-
Descrição: dc.descriptionGlobal Fishing Watch-
Descrição: dc.descriptionCentre for Statistics in Ecology Environment and Conservation Department of Statistical Sciences University of Cape Town-
Descrição: dc.descriptionSchool of Agriculture Food and Ecosystem Sciences The University of Melbourne-
Descrição: dc.descriptionCentre for Research into Ecological and Environmental Modelling Mathematics and Statistics University of St Andrews-
Descrição: dc.descriptionSchool of Mathematics and Statistics Victoria University of Wellington-
Descrição: dc.descriptionOrganismic and Evolutionary Biology Graduate Program University of Massachusetts at Amherst-
Descrição: dc.descriptionDepartment of Natural Resources Science University of Rhode Island-
Descrição: dc.descriptionVertebrate Pest Research Unit Department of Primary Industries NSW-
Descrição: dc.descriptionInstitute of Biosciences São Paulo State University, São Paulo-
Descrição: dc.descriptionStatistical Ecotoxicology University of Bayreuth-
Descrição: dc.descriptionTheoretical Ecology University of Regensburg-
Descrição: dc.descriptionDepartment of Biological Sciences University of Alberta-
Descrição: dc.descriptionInstitute of Biosciences São Paulo State University, São Paulo-
Descrição: dc.descriptionNational Research Foundation: 114696-
Descrição: dc.descriptionFAPESP: 17/16650-5-
Formato: dc.format266-281-
Idioma: dc.languageen-
Relação: dc.relationMethods in Ecology and Evolution-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectHARKing-
Palavras-chave: dc.subjectmodel assumptions-
Palavras-chave: dc.subjectp-hacking-
Palavras-chave: dc.subjectp-values-
Palavras-chave: dc.subjectpre-registration-
Palavras-chave: dc.subjectquestionable research practices-
Palavras-chave: dc.subjectreproducibility crisis-
Palavras-chave: dc.subjectresearch waste-
Título: dc.titleFour principles for improved statistical ecology-
Tipo de arquivo: dc.typevídeo-
Aparece nas coleções:Repositório Institucional - Unesp

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