Logarithmic Axis Graphs Distort Lay Judgment

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