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.
journal_name
R Soc Open Scijournal_title
Royal Society open scienceauthors
Yanco SW,McDevitt A,Trueman CN,Hartley L,Wunder MBdoi
10.1098/rsos.200231subject
Has Abstractpub_date
2020-06-03 00:00:00pages
200231issue
6issn
2054-5703pii
rsos200231journal_volume
7pub_type
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