@techreport{lin_fooled_2021,
	title = {Fooled by beautiful data: {Visualization} aesthetics bias trust in science, news, and social media},
	shorttitle = {Fooled by beautiful data},
	url = {https://psyarxiv.com/dnr9s/},
	abstract = {Scientists, policymakers, and the public increasingly rely on data visualizations – such as COVID tracking charts, weather forecast maps, and political polling graphs – to inform important decisions. The aesthetic decisions of graph-makers may produce graphs of varying visual appeal, independent of data quality. Here we tested whether the beauty of a graph influences how much people trust it. Across three studies, we sampled graphs from social media, news reports, and scientific publications, and consistently found that graph beauty predicted trust. In a fourth study, we manipulated both the graph beauty and misleadingness. We found that beauty, but not actual misleadingness, causally affected trust. These findings reveal a source of bias in the interpretation of quantitative data and indicate the importance of promoting data literacy in education.},
	language = {en-us},
	urldate = {2022-01-24},
	institution = {PsyArXiv},
	author = {Lin, Chujun and Thornton, Mark Allen},
	month = dec,
	year = {2021},
	doi = {10.31234/osf.io/dnr9s},
	note = {type: article},
	keywords = {Aesthetics, Beauty-is-good Stereotype, Causal Effects, Data Visualizations, Psychology, Public Trust, Publication Bias, Social and Behavioral Sciences, other},
}

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