A Pragmatic Approach to Biases in Visual Data Analysis

Toni Verbeiren, Ryo Sakai and Jan Aerts, ESAT/STADIUS – KU Leuven, Belgium

Visual biases and more generally cognitive biases are a part of human life. Often times to the frustration of the rational decision makers we aspire to be. Research into these biases has sparked a recent burst in interest, and more and more people are aware of possible pitfalls. Nevertheless, when visualizations are used in data analysis, one may still be sceptical. In our work we argue that the consequences of biases during data analysis have to be considered rather then their occurrences by itself. By doing so, we motivate a situation where cognitive research offers a foundation to reduce common mistakes while not stepping into the trap of attempting to over-engineering the visualizations that we create.

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