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.
Highlights
Subject Category: Ecology, conservation, and global change biology Subject Areas: ecology Keywords: multiple hypotheses, simulation models, modelling, inference, scientific method
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
Summary
The ultimate goal of science is to learn about the relationships between observable patterns in the world around us and the processes that generate those patterns. One potentially underappreciated limitation of NHST is that it does not produce evidential support for hypotheses, instead providing only weak evidence of incongruence between observed data and a null hypothesis [12]. The practical recommendations in our approach are intended to facilitate wider adoption of multiple hypothesis methods, guard against inferential errors to which multi-hypothesis methods are still prone and provide a formal framework for such analyses This combination of multi-hypothesis inference and pre-data collection modelling represents a powerful alternative incarnation of the scientific method geared towards stronger inference that is less susceptible to errors arising from unconsidered processes. The steps are: 1. specify candidate hypotheses; 2. write a model for each hypothesis; 3. generate sampling distributions of simulated data from each hypothesis; 4. quantify the variance within and overlap between sampling distributions; and 5. revise hypotheses as necessary and repeat steps 1–4
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