Biased Average Position Estimates in Line and Bar Graphs: Underestimation, Overestimation, and Perceptual Pull
This research addresses a fundamental issue in data visualization for designers and users, revealing that even high-precision encodings like position can lead to biased interpretations, which is incremental as it builds on known precision advantages.
The study tackled the problem of systematic biases in visual data encoding using position (line and bar heights), finding that average position estimates are significantly biased with lines underestimated and bars overestimated, and showing a 'perceptual pull' effect in multi-series displays.
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.