Anchors and ratios to quantify and explain y-axis distortion effects in graphs.

Abstract

Data visualizations are common in publications addressed to scientists and the general public. A common graph distortion effect can be obtained by changing the y-axis range. On bar graphs with lower truncated scales (the y-axis starting point is above the data origin), observers tend to perceive larger differences between the values depicted. Herein, we define anchors, information that can be perceived from a graph, to explain ratings of differences in bar graphs. Study 1 examined whether the upper y-axis truncation effect exists or not. We confirmed its existence even though the effect size is smaller compared to lower y-axis truncation effect. Study 2 examined lower and upper y-axis truncations and expansions. We found that, compared to graphs without distortions, observers perceive larger differences between values when there is truncation and smaller differences when there is expansion at either end of the y-axis. Study 3 examined whether the effects of lower and upper y-axis distortions are also present on reversed bar graphs. We found that the black bars biased observers more when they are truncated, as it reduces their area. Finally, Study 4 examined the impact of y-axis distortions on bar graphs, dot graphs, and line graphs. We found that a plot not showing bars results in less biased judgments in the presence of truncation and similar biases for lower and upper truncation. We discuss the results of other relevant research using these anchors and argue that characterizing graphs using the anchors proposed herein can be generalized to other data visualizations. (PsycInfo Database Record (c) 2025 APA, all rights reserved).