Displaying proportions and percentages
Abstract
Pie and bar charts are commonly used to display percentage or proportional data, but professional data analysts have frowned on the use of the pie chart on the grounds that judgements of area are less accurate than judgements of lenth. Thus the bar chart has been favoured. When the amount of data to be communicated is small, some authorities have advocated the use of properly constructed tables, as another option. The series of experiments reported here suggests that there is little to choose between the pie and the bar chart, with the former enjoying a slight advantage if the required judgement is a complicated one, but that both forms of chart are superior to the table. Thus our results do not support the commonly expressed opinion that pie charts are inferior. An analysis of the nature of the task and a review of the psychophysical literature suggest that the traditional prejudice against the pie chart is misguided.
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Schnürer, R., Ritzi, M., Çöltekin, A., & Sieber, R. (2020) · Information Visualization · doi:10.1177/1473871619896103 · Cited by 11 · see citing works
A Componential Model of Human Interaction with Graphs: VI. Cognitive Engineering of Pie Graphs
Gillan, D. J., & Callahan, A. B. (2000) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872000779698024 · Cited by 11 · see citing works
Task-Based Effectiveness of Basic VisualizationsPreprint
Saket, B., Endert, A., & Demiralp, Ç. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.08546 · Cited by 11 · see citing works
Display of Quantitative Information: Are Grables better than Plain Graphs or Tables?
Hink, J. K., Wogalter, M. S., & Eustace, J. K. (1996) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193129604002302 · Cited by 10 · see citing works
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking RepositoryPreprint
Hu, K., Gaikwad, N., Bakker, M. A., Hulsebos, M., Zgraggen, E., Hidalgo, C. A., Kraska, T., Li, G., Satyanarayan, A., & Demiralp, Ç. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04616 · Cited by 10 · see citing works
Brown, J. R., Doore, S. A., Dimmel, J. K., Giudice, N., & Giudice, N. A. (2023) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3597638.3608429 · Cited by 9 · see citing works
Improving Evaluation Using Visualization Decision-Making Models: A Practical Guide
Bancilhon, M., Padilla, L., & Ottley, A. (2023) · Springer eBooks · doi:10.1007/978-3-031-34738-2_4 · Cited by 7 · see citing works
- Authors
- Spence, I., & Lewandowsky, S.
- Affiliations
- University of Toronto (CA)
- The University of Western Australia (AU)
- Corresponding author
- Spence, Ian
- Venue
- Applied Cognitive Psychology
- Publisher
- Wiley
- Published
- 1991-01
- Volume
- 5
- Issue
- 1
- Pages
- 61-77
- ISSN
- 0888-4080 (ISSN-L)
- 1099-0720
- OpenAlex
- W2059636449
- Cited by
- 162
- References
- 25
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 2 · 2023: 10 · 2022: 5 · 2021: 7 · 2020: 7 · 2019: 9 · 2018: 11 · 2017: 5 · 2016: 7 · 2015: 3 · 2014: 10 · 2013: 6 · 2012: 8
- FWCI
- 3.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Cognitive Psychology (publishedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- pie charts, bar charts, proportional data, data presentation, data tables, psychophysics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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