A modern method of multiple working hypotheses to improve inference in ecology.

Autor: Yanco SW; Department of Integrative Biology, University of Colorado Denver, Denver, CO, USA., McDevitt A; Department of Integrative Biology, University of Colorado Denver, Denver, CO, USA., Trueman CN; Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK., Hartley L; Department of Integrative Biology, University of Colorado Denver, Denver, CO, USA., Wunder MB; Department of Integrative Biology, University of Colorado Denver, Denver, CO, USA.
Jazyk: angličtina
Zdroj: Royal Society open science [R Soc Open Sci] 2020 Jun 03; Vol. 7 (6), pp. 200231. Date of Electronic Publication: 2020 Jun 03 (Print Publication: 2020).
DOI: 10.1098/rsos.200231
Abstrakt: Science provides a method to learn about the relationships between observed patterns and the processes that generate them. However, inference can be confounded when an observed pattern cannot be clearly and wholly attributed to a hypothesized process. Over-reliance on traditional single-hypothesis methods (i.e. null hypothesis significance testing) has resulted in replication crises in several disciplines, and ecology exhibits features common to these fields (e.g. low-power study designs, questionable research practices, etc.). Considering multiple working hypotheses in combination with pre-data collection modelling can be an effective means to mitigate many of these problems. We present a framework for explicitly modelling systems in which relevant processes are commonly omitted, overlooked or not considered and provide a formal workflow for a pre-data collection analysis of multiple candidate hypotheses. We advocate for and suggest ways that pre-data collection modelling can be combined with consideration of multiple working hypotheses to improve the efficiency and accuracy of research in ecology.
Competing Interests: The authors declare no competing interests.
(© 2020 The Authors.)
Databáze: MEDLINE
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