JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1131 10.6339/24-JDS1131 Data Science in Action Evaluating Perceptual Judgements on 3D Printed Bar Charts WiederichTylertwiederich2@huskers.unl.edu1∗ VanderPlasSusan1 Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, United States of America Corresponding author. Email: twiederich2@huskers.unl.edu. 20242452024222176190 Supplementary Material

Stimuli, code, and data for this experiment are provided at https://github.com/TWiedRW/2023-JDS-3dcharts.

182023742024 2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2024 Open access article under the CC BY license.

Graphical design principles typically recommend minimizing the dimensionality of a visualization - for instance, using only 2 dimensions for bar charts rather than providing a 3D rendering, because this extra complexity may result in a decrease in accuracy. This advice has been oft repeated, but the underlying experimental evidence is focused on fixed 2D projections of 3D charts. In this paper, we describe an experiment which attempts to establish whether the decrease in accuracy extends to 3D virtual renderings and 3D printed charts. We replicate the grouped bar chart comparisons in the 1984 Cleveland & McGill study, assessing the accuracy of numerical estimates using different types of 3D and 2D renderings.

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