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

A modern method of multiple working hypotheses to improve inference in ecology
A modern method of multiple working hypotheses to improve inference in ecology
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.
2054-5703
Yanco, Scott W.
91c9a8c0-22b7-4e4c-92f5-f4c2371f7dcb
Mcdevitt, Andrew
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Trueman, Clive N.
d00d3bd6-a47b-4d47-89ae-841c3d506205
Hartley, Laurel
33eb9aaa-9749-4d69-914f-2e64f5096e09
Wunder, Michael B.
cf630c47-54e9-4e3b-a18e-572ff0a2c505
Yanco, Scott W.
91c9a8c0-22b7-4e4c-92f5-f4c2371f7dcb
Mcdevitt, Andrew
6f77726d-cfdf-4b8b-9429-2b2073f00584
Trueman, Clive N.
d00d3bd6-a47b-4d47-89ae-841c3d506205
Hartley, Laurel
33eb9aaa-9749-4d69-914f-2e64f5096e09
Wunder, Michael B.
cf630c47-54e9-4e3b-a18e-572ff0a2c505

Yanco, Scott W., Mcdevitt, Andrew, Trueman, Clive N., Hartley, Laurel and Wunder, Michael B. (2020) A modern method of multiple working hypotheses to improve inference in ecology. Royal Society Open Science, 7 (6), [200231]. (doi:10.1098/rsos.200231).

Record type: Article

Abstract

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.

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Accepted/In Press date: 15 May 2020
Published date: 3 June 2020

Identifiers

Local EPrints ID: 444989
URI: http://eprints.soton.ac.uk/id/eprint/444989
ISSN: 2054-5703
PURE UUID: 5c61e92f-91bb-4623-9ca8-d47df007dd63

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Date deposited: 16 Nov 2020 17:32
Last modified: 25 Nov 2021 23:06

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Contributors

Author: Scott W. Yanco
Author: Andrew Mcdevitt
Author: Laurel Hartley
Author: Michael B. Wunder

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