Logarithmic Axis Graphs Distort Lay Judgment

30 Pages Posted: 20 May 2020

See all articles by William Ryan

William Ryan

University of California, Berkeley - Haas School of Business

Ellen Evers

UC Berkeley, Haas

Date Written: May 5, 2020

Abstract

COVID-19 data is often presented using graphs with either a linear or logarithmic scale. Given the importance of this information, understanding how choice of scale changes interpretations is critical. To test this, we presented laypeople with the same data plotted using differing scales. We found that graphs with a logarithmic, as opposed to linear, scale resulted in laypeople making less accurate predictions of growth, viewing COVID-19 as less dangerous, and expressing both less support for policy interventions and less intention to take personal actions to combat COVID-19. Education reduces, but does not eliminate these effects. These results suggest that public communications should use logarithmic graphs only when necessary, and such graphs should be presented alongside education and linear graphs of the same data whenever possible.

Keywords: COVID-19, COVID-19 Public Communication, Graphical Presentation, Numerical Cognition

JEL Classification: D90, D91, I10, I12, I18

Suggested Citation

Ryan, William and Evers, Ellen, Logarithmic Axis Graphs Distort Lay Judgment (May 5, 2020). Available at SSRN: https://ssrn.com/abstract=3605872 or http://dx.doi.org/10.2139/ssrn.3605872

William Ryan (Contact Author)

University of California, Berkeley - Haas School of Business ( email )

545 Student Services Building, #1900
2220 Piedmont Avenue
Berkeley, CA 94720
United States

Ellen Evers

UC Berkeley, Haas ( email )

Haas School of Business
Berkeley, CA 94720
United States

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