Data Visualization Literacy and Visualization Biases: Cases for Merging Parallel Threads

Abstract

People are prone to many biases when viewing data visualizations. Recent visualization research has uncovered biases that manifest during visualization use, quantified their impact and developed strategies for mitigating such biases. In a parallel thread, visualization research has investigated how to measure a person’s data visualization literacy and examine the performance consequences of individual differences in these literacy measures. The aim of this chapter is to make a case for merging these threads. To bridge the gap, we highlight research in cognitive biases, that has established that there are relationships between the impact of biases and factors such as experience and cognitive ability. Drawing on prior work in visualization biases, we provide examples of how visualization literacy measures may have led to different results in these studies. As research continues to identify and quantify the biases that occur in visualizations, the impact of people’s individual abilities may prove to be an important consideration for analysis and design.