Discussion questions#
There is a tension between communicating the most information and knowledge effectively (Cairo) and working with the constraints of human visual perception (Franconeri et al.). Where does this tension come to a conflict when making data visualizations? Would it be possible to make a practical semi-general checklist that we could use to assess the efficacy of our figures? If so, what would that checklist look like?
Franconeri and colleagues show how framing (e.g., choice of highlighting, annotations, scaling) in data visualization can lead to fundamentally different conclusions from the same graphic. How should you implement a balance between making plotting choices to communicate results efficiently, telling a story about the research, and not biasing viewers to incorrect interpretations? How would this change depend on your target audience and the discipline of the research you are conducting?