Average-Blindness and Ensemble-Acuity: When Plotted Raw Data Conveys Averages Better Than Plotted Averages
Year: 2025
Type: preprint
Abstract: Data visualizations often display averages without underlying data points, aiming to enhance comprehension through visual simplification. Yet the theory that this simplicity improves comprehension remains untested. Using a new drag-and-drop measure, we tested this theory’s most basic prediction: that viewers will locate explicitly plotted, isolated averages more accurately than averages they must estimate from raw data. Remarkably, we found the opposite—accuracy was lower for standard bar or line plots depicting averages than for raw-data plots. This discovery stemmed from two observed phenomena: (1) average-blindness—the mislocation of explicitly marked averages, often placed within bars or along lines rather than at the intended markers; and (2) ensemble-acuity—the accurate estimation of averages from raw data (or ensembles), with variability comparable to confidence intervals and few outright errors. Our findings reveal a paradox of certainty: certainty about the average’s location can obscure it, whereas the uncertainty inherent in raw data can clarify it.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413964680