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When an analyst or scientist has a belief about how the world works, theirthinking can be biased in favor of that belief. Therefore, one bedrockprinciple of science is to minimize that bias by testing the predictions ofone's belief against objective data. But interpreting visualized data is acomplex perceptual and cognitive process. Through two crowdsourced experiments,we demonstrate that supposedly objective assessments of the strength of acorrelational relationship can be influenced by how strongly a viewer believesin the existence of that relationship. Participants viewed scatterplotsdepicting a relationship between meaningful variable pairs (e.g., number ofenvironmental regulations and air quality) and estimated their correlations.They also estimated the correlation of the same scatterplots labeled insteadwith generic 'X' and 'Y' axes. In a separate section, they also reported howstrongly they believed there to be a correlation between the meaningfulvariable pairs. Participants estimated correlations more accurately when theyviewed scatterplots labeled with generic axes compared to scatterplots labeledwith meaningful variable pairs. Furthermore, when viewers believed that twovariables should have a strong relationship, they overestimated correlationsbetween those variables by an r-value of about 0.1. When they believed that thevariables should be unrelated, they underestimated the correlations by anr-value of about 0.1. While data visualizations are typically thought topresent objective truths to the viewer, these results suggest that existingpersonal beliefs can bias even objective statistical values people extract fromdata.

Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation