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Visualization research often focuses on perceptual accuracy or helpingreaders interpret key messages. However, we know very little about how chartdesigns might influence readers' perceptions of the people behind the data.Specifically, could designs interact with readers' social cognitive biases inways that perpetuate harmful stereotypes? For example, when analyzing socialinequality, bar charts are a popular choice to present outcome disparitiesbetween race, gender, or other groups. But bar charts may encourage deficitthinking, the perception that outcome disparities are caused by groups'personal strengths or deficiencies, rather than external factors. These faultypersonal attributions can then reinforce stereotypes about the groups beingvisualized. We conducted four experiments examining design choices thatinfluence attribution biases (and therefore deficit thinking). Crowdworkersviewed visualizations depicting social outcomes that either mask variability indata, such as bar charts or dot plots, or emphasize variability in data, suchas jitter plots or prediction intervals. They reported their agreement withboth personal and external explanations for the visualized disparities.Overall, when participants saw visualizations that hide within-groupvariability, they agreed more with personal explanations. When they sawvisualizations that emphasize within-group variability, they agreed less withpersonal explanations. These results demonstrate that data visualizations aboutsocial inequity can be misinterpreted in harmful ways and lead to stereotyping.Design choices can influence these biases: Hiding variability tends to increasestereotyping while emphasizing variability reduces it.

Dispersion vs Disparity: Hiding Variability Can Encourage Stereotyping When Visualizing Social Outcomes