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Perceptual proxies for extracting averages in data visualizations
[post]
2018
unpublished
Across science, education, and business, we process and communicate data visually. One bedrock finding in data visualization research is a hierarchy of precision for perceptual encodings of data, e.g., that encoding data with Cartesian positions allows more precise comparisons than encoding with sizes. But his hierarchy has only been tested for single value comparisons, under the assumption that those lessons would extrapolate to multi-value comparisons. We show that when comparing averages
doi:10.31234/osf.io/hsbpj
fatcat:wpzddpekzvhfjpqaijodmy2s4e