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Description: False polarization refers to the phenomenon that people perceive the views of opposing political groups to be more extreme (and less diverse) than they actually are. These partisan misperceptions can sustain, and potentially accelerate, actual ideological and affective polarization, as beliefs about extremism in the opposing party may be used to justify more extreme positions or actions by one’s own party. Past work has sought to correct partisan misperceptions by informing people about the actual, typical views held by members of the opposing party. Previous studies suggest that, in addition to correcting misperceptions of the other group, such interventions may also decrease individuals’ own support for extreme positions (e.g., political violence). The present project aims to address a key limitation of previous work: Existing interventions have focused solely on communicating the average (or most common) view held by members of the opposing party, without including information about the variability or distribution of such views. This focus on the central tendency may leave individuals with a distorted understanding of the actual support for extreme views (e.g., effectively downplaying the small but growing portion of the public that expresses support for political violence). Failing to communicate the range of views may also decrease the perceived credibility of the intervention, as has been seen in other work on science communication. The present study examines alternative strategies for communicating the views of the opposing political party through data visualizations, with a particular focus on the effects of visual uncertainty representations. The main research questions concern how including information about the variability of views (e.g., via uncertainty intervals or plotting of individual responses) impacts the correction effect seen in earlier work. We also explore the role of perceived surprise and perceived credibility of the data visualizations as factors that mediate these correction effects.