Work

Truncating bar graphs persistently misleads viewers.

Year: 2021

Type: article

Abstract: Data visualizations and graphs are increasingly common in both scientific and mass media settings. While graphs are useful tools for communicating patterns in data, they also have the potential to mislead viewers. In five studies, we provide empirical evidence that y-axis truncation leads viewers to perceive illustrated differences as larger (i.e., a truncation effect). This effect persisted after viewers were taught about the effects of y-axis truncation and was robust across participants, with 83.5% of participants across these 5 studies showing a truncation effect. We also found that individual differences in graph literacy failed to predict the size of individuals’ truncation effects. PhD students in both quantitative fields and the humanities were susceptible to the truncation effect, but quantitative PhD students were slightly more resistant when no warning about truncated axes was provided. We discuss the implications of these results for the underlying mechanisms and make practical recommendations for training critical consumers and creators of graphs.

Source: Journal of Applied Research in Memory and Cognition

DOI: 10.1016/j.jarmac.2020.10.002

Authors: Brenda W. Yang, Camila Vargas Restrepo, Matthew L. Stanley, Elizabeth J. Marsh

Institution: Duke University

Language: English

Topic: Data Visualization and Analytics

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, James S. McDonnell Foundation, Duke University

OpenAlex ID: W3129260127

FWCI: 1.528

Cites: 67

Cited by: 33

Related to: 10