Predicting a win by a small margin: The effect of graphic scaling in published polls on voters' predictions

Abstract

The public display of election poll results is often manipulated to influence voter predictions about the race. Narrow scaling is one such manipulation that involves truncating the chart's vertical axis such that its range extends closely around the values of the bars. This manipulation exacerbates the visual difference between bars, making the margin appear larger than an unbiased representation would suggest. The current research examines whether narrow scaling of a bar chart depicting the degree of support for political candidate affects voters' predictions about election outcomes. In three experiments, conducted during the 2022 US gubernatorial and senate elections, we displayed published polls to potential voters using a wide- or a narrow-scaled bar chart. We found that when the scale is narrow, voters are more likely to predict that the leading candidate in the poll will win the election and by a larger margin. This scaling bias occurs despite voters' relative skepticism about narrow-scaled polls. We further find that the scaling effect is attenuated when the poll margin is relatively large and enhanced when numerical value labels are removed from the graphic display.