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Motivated numeracy and enlightened self-government

Published online by Cambridge University Press:  31 May 2017

DAN M. KAHAN*
Affiliation:
Yale University, USA
ELLEN PETERS
Affiliation:
The Ohio State University, USA
ERICA CANTRELL DAWSON
Affiliation:
Cornell University, USA
PAUL SLOVIC
Affiliation:
Decision Research & University of Oregon, USA
*
*Correspondence to: Yale Law School, PO Box 208215, New Haven, CT 06520, USA. Email: dan.kahan@yale.edu

Abstract

Why does public conflict over societal risks persist in the face of compelling and widely accessible scientific evidence? We conducted an experiment to probe two alternative answers: the ‘science comprehension thesis’ (SCT), which identifies defects in the public's knowledge and reasoning capacities as the source of such controversies; and the ‘identity-protective cognition thesis’ (ICT), which treats cultural conflict as disabling the faculties that members of the public use to make sense of decision-relevant science. In our experiment, we presented subjects with a difficult problem that turned on their ability to draw valid causal inferences from empirical data. As expected, subjects highest in numeracy – a measure of the ability and disposition to make use of quantitative information – did substantially better than less numerate ones when the data were presented as results from a study of a new skin rash treatment. Also as expected, subjects’ responses became politically polarized – and even less accurate – when the same data were presented as results from the study of a gun control ban. But contrary to the prediction of SCT, such polarization did not abate among subjects highest in numeracy; instead, it increased. This outcome supported ICT, which predicted that more numerate subjects would use their quantitative-reasoning capacity selectively to conform their interpretation of the data to the result most consistent with their political outlooks. We discuss the theoretical and practical significance of these findings.

Type
Articles
Copyright
Copyright © Cambridge University Press 2017 

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