Average estimates in line graphs are biased toward areas of higher variability

Image of three time series charts. The first encodes data with points equally spaced along the x-axis, the second uses a line, and the third uses points equally spaced along the arc of the line. On each chart, there is a grey overlaid line indicating the true average of the data and a red line showing participants' estimated average.
Demonstration of the bias toward variability for three mark types showing the same data. The red line shows the mean estimated averages across all participants in our second experiment. The line chart (center) shows a bias of the estimated average toward higher variability in the higher y-values. The bias is smallest when the data is shown as points equally spaced along the x-axis (left). The bias in line charts is in the same direction as the bias of estimates of points sampled at equal intervals along the arc of the line (right).
Abstract
We investigate variability overweighting, a previously undocumented bias in line graphs, where estimates of average value are biased toward areas of higher variability in that line. We found this effect across two preregistered experiments with 140 and 420 participants. These experiments also show that the bias is reduced when using a dot encoding of the same series. We can model the bias with the average of the data series and the average of the points drawn along the line. This bias might arise because higher variability leads to stronger weighting in the average calculation, either due to the longer line segments (even though those segments contain the same number of data values) or line segments with higher variability being otherwise more visually salient. Understanding and predicting this bias is important for visualization design guidelines, recommendation systems, and tool builders, as the bias can adversely affect estimates of averages and trends.
Materials
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Authors
Dominik Moritz
Francis Nguyen
Steven L. Franconeri
Citation
Thumbnail image for publication titled: Average estimates in line graphs are biased toward areas of higher variability
Average estimates in line graphs are biased toward areas of higher variability

Dominik Moritz, Lace M. Padilla, Francis Nguyen, and Steven L. Franconeri. IEEE Transactions on Visualization and Computer Graphics—VIS/TVCG. 2023.

PDF | Preprint | Supplement | Preregistration | Code | Video preview | Award | BibTeX | VIS 2023 Best Paper Honorable Mention!


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