Biased Average Position Estimates in Line and Bar Graphs: Underestimation, Overestimation, and Perceptual Pull

TL;DR

This paper demonstrates that despite being a precise visual encoding, position in line and bar graphs can produce systematic biases in average position estimates, including underestimation, overestimation, and perceptual pull effects.

Contribution

The study reveals systematic biases in average position perception in graphs, challenging the assumption of position's infallible accuracy in data visualization.

Findings

Line positions are underestimated in reports.

Bar positions are overestimated in reports.

Perceptual pull causes estimates to be biased toward other series.

Abstract

In visual depictions of data, position (i.e., the vertical height of a line or a bar) is believed to be the most precise way to encode information compared to other encodings (e.g., hue). Not only are other encodings less precise than position, but they can also be prone to systematic biases (e.g., color category boundaries can distort perceived differences between hues). By comparison, position's high level of precision may seem to protect it from such biases. In contrast, across three empirical studies, we show that while position may be a precise form of data encoding, it can also produce systematic biases in how values are visually encoded, at least for reports of average position across a short delay. In displays with a single line or a single set of bars, reports of average positions were significantly biased, such that line positions were underestimated and bar positions were overestimated. In displays with multiple data series (i.e., multiple lines and/or sets of bars), this systematic bias still persisted. We also observed an effect of "perceptual pull", where the average position estimate for each series was 'pulled' toward the other. These findings suggest that, although position may still be the most precise form of visual data encoding, it can also be systematically biased.