Event Description:
Your visual system can crunch vast arrays of numbers at a glance, providing the rest of your brain with critical values, statistics, and patterns needed to make decisions about your data. But that process can be derailed by biases at both the perceptual and cognitive levels. Cindy Xiong demonstrates 3 instances of these biases that obstruct effective data communication. First, in the most frequently used graphs – lines and bars – reproductions of average values are systematically underestimated for lines, and overestimated for bars. Second, when people see correlational data, they often mistakenly also see a causal relationship. She’ll show how this error can be even worse for some types of graphs. Third, we’ve all experienced being overwhelmed by a confusing visualization. This may happen because the designer – an expert in the topic – thinks that you’d see what they see. Cindy Xiong describes a replication of this real-world phenomenon in the lab, showing that, when communicating patterns in data to others, it is tough for people to see a visualization from a naive perspective. She discusses why these biases happen in our brains, and prescribe ways to design visualizations to mitigate these biases.
Event Speaker:
Cindy Xiong, PhD Candidate in the Department of Psychology, Northwestern University
Event Information:
Free and open to the public but registration is required via the event webpage. Hosted by the Data Science Institute as part of their Data for Good series. Please contact the Data Science Institute at [email protected] with any questions.
