Proceedings of the Annual Meeting of the Cognitive Science Society

The increase of misinformation in the public sphere over thepast decade represents an urgent societal issue, given thechallenge of distinguishing veridical facts from false ormisleading information. The present experiment’s resultsindicate that people are reliant on numerical information in theirdetermination of whether a statistic related to global warming isrepresentative or misleading. Of particularly practicalsignificance, the results also demonstrate that showingparticipants a mixed set of revealing and misleading globalwarming statistics leads to an increase in global warmingacceptance, rather than sowing confusion (or some sense that alldata are equally dubious or compelling). Replicating priorresults, nationalism and global warming acceptance are in anegative relationship. We also describe the background, design,and assessment of a curriculum intended to help the generalpublic better distinguish between representative and misleadingstatistics about anthropogenic climate change. The findingshighlight numerically-driven inferencing as a useful paradigmfor the assessment of information relating to global warming andenvironmental risk.