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.

翻译：我们研究了折线图中一种此前未被记录的偏差——变异过度加权现象，即平均值估计会向折线变异较高的区域偏移。通过两项预注册实验（分别包含140名和420名参与者），我们验证了这一效应。实验同时表明，当采用同一数据序列的点编码方式时，该偏差会有所减弱。我们可以通过数据序列的平均值与沿折线绘制点的平均值来建模这一偏差。这种偏差可能源于高变异区域在平均值计算中获得更高权重——这既可能是由于较长的折线段（尽管这些线段包含相同数量的数据值），也可能是由于高变异折线段在视觉上更为突出。理解并预测这一偏差对于可视化设计规范、推荐系统及工具开发具有重要价值，因为该偏差可能对平均值和趋势的估算产生不利影响。