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Abstract
How do we know if the data visualizations we create are effective at communicating our intended result? With advancing technology, there are an infinitely many number of design choices to make such as variables to display, shapes and colors, scales, and faceting. One way to evaluate design choices is through the use of graphical tests. We could ask participants to identify differences in graphs, read information off of a chart accurately, use data to make correct real-world decisions, or predict the next few observations. All of these types of tests require different levels of use and manipulation of the information being presented in the chart. In this presentation, I will introduce the lineup protocol (Buja et. al 2009), which established a framework for visual inference. I will then share my current research which validates ‘You Draw It’, an interactive feature introduced in the New York Times (2015), as a method for graphical testing by comparing results to the less technological method utilized in Mosteller et al. (1981). The results of graphical testing allow us to provide guidelines for creating effective charts, thus advancing communication of science and data to the public.