  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.
