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.