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Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
An increasing number of voices highlight the need for science itself to transform and to engage in the co-production of knowledge and action, in order to enable the fundamental transformations needed to advance towards sustainable futures. But how can global sustainability-oriented research networks engage in co-production of knowledge and action? The present article introduces a strategic tool called the ‘network compass’ which highlights four generic, interrelated fields of action through which networks can strive to foster co-production. It is based on the networks’ particular functions and how these can be engaged for co-production processes. This tool aims to foster self-reflection and learning within and between networks in the process of (re)developing strategies and activity plans and effectively contributing to sustainability transformations.