Bar graphs depicting averages are perceptually misinterpreted: The within-the-bar bias
Open accessAbstract
Perhaps the most common method of depicting data, in both scientific communication and popular media, is the bar graph. Bar graphs often depict measures of central tendency, but they do so asymmetrically: A mean, for example, is depicted not by a point, but by the edge of a bar that originates from a single axis. Here we show that this graphical asymmetry gives rise to a corresponding cognitive asymmetry. When viewers are shown a bar depicting a mean value and are then asked to judge the likelihood of a particular data point being part of its underlying distribution, viewers judge points that fall within the bar as being more likely than points equidistant from the mean, but outside the bar--as if the bar somehow "contained" the relevant data. This "within-the-bar bias" occurred (a) for graphs with and without error bars, (b) for bars that originated from both lower and upper axes, (c) for test points with equally extreme numeric labels, (d) both from memory (when the bar was no longer visible) and in online perception (while the bar was visible during the judgment), (e) both within and between subjects, and (f) in populations including college students, adults from the broader community, and online samples. We posit that this bias may arise due to principles of object perception, and we show how it has downstream implications for decision making.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In bar graphs depicting average values, people judge data values that fall within the bar as being more likely to be part of the data set than points outside the bar, despite being equidistant from the mean.
“In bar graphs depicting average values, people judge data values that fall within the bar as being more likely to be part of the data set than points outside the bar, despite being equidistant from the mean [30, 6].”
From Illusion of Causality in Visualized Data (2020) · cites this paper as [30]
Bar encodings are visually vertically asymmetrical, with the area below the mean filled and the area above unfilled.
“Bar encodings, in contrast, are visually vertically asymmetrical, with the area below the mean filled and the area above unfilled.”
From Illusion of Causality in Visualized Data (2020) · cites this paper as [30]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The visual display of quantitative information
(1985) · General Pharmacology The Vascular System · doi:10.1016/0306-3623(85)90230-7 · Cited by 5,090 · see citing works
The Visual Display of Quantitative Information.
Zelterman, D., & Tufte, E. R. (1984) · Journal of the American Statistical Association · doi:10.2307/2288294 · Cited by 2,088 · see citing works
Egly, R., Driver, J., & Rafal, R. D. (1994) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.123.2.161 · Cited by 1,174 · see citing works
Objects and attention: the state of the art
Scholl, B. J. (2001) · Cognition · doi:10.1016/s0010-0277(00)00152-9 · Cited by 975 · see citing works
Self-citationEgly, R., Driver, J., & Rafal, R. D. (1994) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.123.2.161 · Cited by 942 · see citing works
Gigerenzer, G., Swijtink, Z. G., Porter, T. M., Daston, L., Beatty, J., & Krüger, L. (1989) · Cambridge University Press eBooks · doi:10.1017/cbo9780511720482 · Cited by 770 · see citing works
The Empire of chance: how probability changed science and everyday life
(1989) · Choice Reviews Online · doi:10.5860/choice.27-2084 · Cited by 600 · see citing works
Bars and lines: A study of graphic communication
Zacks, J., & Tversky, B. (1999) · Memory & Cognition · doi:10.3758/bf03201236 · Cited by 300 · see citing works
Graph Design for the Eye and Mind
Kosslyn, S. M. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195311846.001.0001 · Cited by 298 · see citing works
Effects of numerical and graphical displays on professed risk-taking behavior.
Stone, E. R., Yates, J. F., & Parker, A. M. (1997) · Journal of Experimental Psychology Applied · doi:10.1037/1076-898x.3.4.243 · Cited by 181 · see citing works
Foreground:background salience: Explaining the effects of graphical displays on risk avoidance
Stone, E. R., Sieck, W. R., Bull, B. E., Yates, J. F., Parks, S. C., & Rush, C. J. (2003) · Organizational Behavior and Human Decision Processes · doi:10.1016/s0749-5978(03)00003-7 · Cited by 167 · see citing works
Knowledge and Information Visualization: searching for synergies
Tergan, S., & Keller, T. (2005) · Cited by 125 · see citing works
Recalling an event one year later: the impact of props, drawing and a prior interview
Salmon, K., & Pipe, M. (2000) · Applied Cognitive Psychology · doi:10.1002/(sici)1099-0720(200003/04)14:2 · Cited by 113 · see citing works
The role of closure in defining the “objects” of object-based attention
Marino, A. C., & Scholl, B. J. (2005) · Perception & Psychophysics · doi:10.3758/bf03193547 · Cited by 105 · see citing works
Self-citationReading bar graphs: Effects of extraneous depth cues and graphical context.
Zacks, J. M., Levy, E., Tversky, B., & Schiano, D. J. (1998) · Journal of Experimental Psychology Applied · doi:10.1037/1076-898x.4.2.119 · Cited by 102 · see citing works
Automatic, stimulus-driven attentional capture by objecthood
Kimchi, R., Yeshurun, Y., & Cohen-Savransky, A. (2007) · Psychonomic Bulletin & Review · doi:10.3758/bf03194045 · Cited by 89 · see citing works
Risk avoidance: Graphs versus numbers
Chua, H. F., Yates, J. F., & Shah, P. (2006) · Memory & Cognition · doi:10.3758/bf03193417 · Cited by 85 · see citing works
Minimalism and the Syntax of Graphs
Gillan, D. J., & Richman, E. (1994) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/001872089403600405 · Cited by 46 · see citing works
Representational Correspondence as a Basic Principle of Diagram Design
Chabris, C. F., & Kosslyn, S. M. (2005) · Lecture notes in computer science · doi:10.1007/11510154_3 · Cited by 38 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Communicating uncertainty about facts, numbers and science
van der Bles, A. M., van der Linden, S., Freeman, A. L. J., Mitchell, J., Galvao, A. B., Zaval, L., & Spiegelhalter, D. J. (2019) · Royal Society Open Science · doi:10.1098/rsos.181870 · Cited by 456 · see citing works
The Science of Visual Data Communication: What Works
Franconeri, S., Padilla, L., Shah, P., Zacks, J. M., & Hullman, J. R. (2021) · Gothic.net · doi:10.1177/15291006211051956 · Cited by 398 · see citing works
Fair Statistical Communication in HCI
Dragicevic, P. (2016) · Human-computer interaction series · doi:10.1007/978-3-319-26633-6_13 · Cited by 339 · see citing works
Decision making with visualizations: a cognitive framework across disciplines
Padilla, L., Creem-Regehr, S. H., Hegarty, M., & Stefanucci, J. K. (2018) · Cognitive Research Principles and Implications · doi:10.1186/s41235-018-0120-9 · Cited by 341 · see citing works
Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error
Correll, M., & Gleicher, M. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346298 · Cited by 258 · see citing works
Hullman, J., Resnick, P., & Adar, E. (2015) · PLOS ONE · doi:10.1371/journal.pone.0142444 · Cited by 194 · see citing works
In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation
Hullman, J., Qiao, X., Correll, M., Kale, A., & Kay, M. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864889 · Cited by 187 · see citing works
The effects of visualization on judgment and decision-making: a systematic literature review
Eberhard, K. (2021) · Management Review Quarterly · doi:10.1007/s11301-021-00235-8 · Cited by 150 · see citing works
ggdist: Visualizations of Distributions and Uncertainty in the Grammar of Graphics
Kay, M. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3327195 · Cited by 185 · see citing works
Why Is the Crowd Divided? Attribution for Dispersion in Online Word of Mouth
He, S. X., & Bond, S. (2015) · Journal of Consumer Research · doi:10.1086/680667 · Cited by 171 · see citing works
Non-expert interpretations of hurricane forecast uncertainty visualizations
Ruginski, I. T., Boone, A. P., Padilla, L., Liu, L., Heydari, N. D., Kramer, H., Hegarty, M., Thompson, W. B., House, D. H., & Creem-Regehr, S. H. (2016) · Spatial Cognition and Computation · doi:10.1080/13875868.2015.1137577 · Cited by 134 · see citing works
Four Experiments on the Perception of Bar Charts
Talbot, J., Setlur, V., & Anand, A. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346320 · Cited by 127 · see citing works
Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data
Kale, A., Nguyen, F., Kay, M., & Hullman, J. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864909 · Cited by 122 · see citing works
Understanding human-data interaction: Literature review and recommendations for design
Victorelli, E. Z., dos Reis, J. C., Hornung, H., & Prado, A. B. (2019) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2019.09.004 · Cited by 109 · see citing works
Effects of ensemble and summary displays on interpretations of geospatial uncertainty data
Padilla, L., Ruginski, I. T., & Creem-Regehr, S. H. (2017) · Cognitive Research Principles and Implications · doi:10.1186/s41235-017-0076-1 · Cited by 105 · see citing works
Reveal, Don’t Conceal: Transforming Data Visualization to Improve Transparency
Weissgerber, T. L., Winham, S. J., Heinzen, E. P., Milin-Lazovic, J. S., Garcia-Valencia, O., Bukumiric, Z., Savic, M. D., Garovic, V. D., & Milic, N. M. (2019) · Circulation · doi:10.1161/circulationaha.118.037777 · Cited by 101 · see citing works
Surfacing Visualization Mirages
McNutt, A. M., Kindlmann, G., & Correll, M. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376420 · Cited by 95 · see citing works
Fundel, V. J., Fleischhut, N., Herzog, S. M., Göber, M., & Hagedorn, R. (2019) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3482 · Cited by 95 · see citing works
Padilla, L., Kay, M., & Hullman, J. R. (2021) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat08296 · Cited by 87 · see citing works
Alhadad, S. S. J. (2018) · Journal of Learning Analytics · doi:10.18608/jla.2018.52.5 · Cited by 85 · see citing works
The good, the bad, and the biased
Szafir, D. A. (2018) · interactions · doi:10.1145/3231772 · Cited by 73 · see citing works
Illusion of Causality in Visualized Data
Xiong, C., Shapiro, J., Hullman, J., & Franconeri, S. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934399 · Cited by 71 · see citing works
Correll, M., & Heer, J. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025922 · Cited by 63 · see citing works
An Evaluation of the Impact of Visual Embellishments in Bar Charts
Skau, D., Harrison, L., & Kosara, R. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12634 · Cited by 61 · see citing works
Epp, C. D., & Bull, S. (2015) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2015.2411604 · Cited by 55 · see citing works
Data Visualization Using R for Researchers Who Do Not Use R
Nordmann, E., McAleer, P., Toivo, W., Paterson, H., & DeBruine, L. M. (2022) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459221074654 · Cited by 44 · see citing works
Thinking Outside the Box: Developing Dynamic Data Visualizations for Psychology with Shiny
Ellis, D. A., & Merdian, H. L. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01782 · Cited by 39 · see citing works
The influence of different graphical displays on nonexpert decision making under uncertainty.
Padilla, L., Hansen, G., Ruginski, I. T., Kramer, H., Thompson, W. B., & Creem-Regehr, S. H. (2014) · Journal of Experimental Psychology Applied · doi:10.1037/xap0000037 · Cited by 39 · see citing works
Castro, S. C., Quinan, P. S., Hosseinpour, H., & Padilla, L. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114803 · Cited by 37 · see citing works
Ripberger, J., Bell, A., Fox, A., Forney, A., Livingston, W., Gaddie, C., Silva, C. L., & JENKINS-SMITHa, H. (2022) · Weather, Climate, and Society · doi:10.1175/wcas-d-21-0034.1 · Cited by 20 · see citing works
The powerful influence of marks: Visual and knowledge-driven processing in hurricane track displays.
Padilla, L., Creem-Regehr, S. H., & Thompson, W. B. (2019) · Journal of Experimental Psychology Applied · doi:10.1037/xap0000245 · Cited by 33 · see citing works
Zhang, S., Heck, P. R., Meyer, M. N., Chabris, C. F., Goldstein, D. G., & Hofman, J. M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2302491120 · Cited by 30 · see citing works
Biasing and debiasing health decisions with bar graphs: Costs and benefits of graph literacy
Okan, Y., García‐Retamero, R., Cokely, E. T., & Maldonado, A. (2018) · Quarterly Journal of Experimental Psychology · doi:10.1177/1747021817744546 · Cited by 29 · see citing works
Perceptual proxies for extracting averages in data visualizations
Yuan, L., Haroz, S., & Franconeri, S. (2018) · Psychonomic Bulletin & Review · doi:10.3758/s13423-018-1525-7 · Cited by 26 · see citing works
Cui, L., & Liu, Z. (2021) · Attention Perception & Psychophysics · doi:10.3758/s13414-020-02212-x · Cited by 26 · see citing works
Holder, E., & Xiong, C. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209377 · Cited by 25 · see citing works
Evaluating the Impact of Uncertainty Visualization on Model Reliance
Zhao, J. L., Wang, Y., Mancenido, M. V., Chiou, E. K., & Maciejewski, R. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3251950 · Cited by 26 · see citing works
A review of uncertainty visualization errors: Working memory as an explanatory theory
Padilla, L. M. K., Castro, S., & Hosseinpour, H. (2021) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/bs.plm.2021.03.001 · Cited by 17 · see citing works
(2015) · HAL · Cited by 20 · see citing works
Effects of Uncertainty Visualization on Attention, Arousal, and Judgment
Rose, A. M., Rose, J. M., Rotaru, K., Sanderson, K., & Thibodeau, J. C. (2022) · Behavioral Research in Accounting · doi:10.2308/bria-2021-011 · Cited by 18 · see citing works
A graph for every analysis: Mapping visuals onto common analyses using flexplot
Fife, D. A., Longo, G., Correll, M., & Tremoulet, P. D. (2021) · Behavior Research Methods · doi:10.3758/s13428-020-01520-2 · Cited by 17 · see citing works
Replacing bar graphs of continuous data with more informative graphics: are we making progress?
Riedel, N., Schulz, R., Kazezian, V., & Weissgerber, T. L. (2022) · Clinical Science · doi:10.1042/cs20220287 · Cited by 18 · see citing works
Kerns, S. H., & Wilmer, J. B. (2021) · Journal of Vision · doi:10.1167/jov.21.12.17 · Cited by 16 · see citing works
Visual comparison of two data sets: Do people use the means and the variability?
Kramer, R. S. S., Telfer, C. G. R., & Towler, A. (2017) · Journal of Numerical Cognition · doi:10.5964/jnc.v3i1.100 · Cited by 13 · see citing works
Karjack, S., Brudzinski, M. R., & Shipley, T. F. (2022) · Seismological Research Letters · doi:10.1785/0220210318 · Cited by 11 · see citing works
The influence of graphical format on judgmental forecasting accuracy: Lines versus points
Theocharis, Z., Smith, L. A., & Harvey, N. (2018) · Futures & Foresight Science · doi:10.1002/ffo2.7 · Cited by 11 · see citing works
The Meta-Analytic Rain Cloud Plot: A New Approach to Visualizing Clearinghouse Data
Fitzgerald, K. G., & Tipton, E. (2022) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2022.2031366 · Cited by 10 · see citing works
Visualizing Uncertainty in Hurricane Forecasts with Animated Risk Trajectories
Witt, J. K., Labe, Z. M., Warden, A. C., & Clegg, B. A. (2023) · Weather Climate and Society · doi:10.1175/wcas-d-21-0173.1 · Cited by 10 · see citing works
Communication of Statistics and Evidence in Times of Crisis
Schneider, C., Kerr, J. R., Dryhurst, S., & Aston, J. (2023) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-040722-052011 · Cited by 14 · see citing works
McDowell, M., & Kause, A. (2021) · Risk Analysis · doi:10.1111/risa.13739 · Cited by 12 · see citing works
Histogram Distortion Bias in Consumer Choices
Lu, T., Yuan, M., Wang, C. A., & Zhang, X. (2022) · Management Science · doi:10.1287/mnsc.2022.4306 · Cited by 9 · see citing works
Fitzgerald, K. G., & Tipton, E. (2023) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2023.2209082 · Cited by 11 · see citing works
The Powerful Influence of Marks: Visual and Knowledge-Driven Processing in Hurricane Track DisplaysPreprint
Padilla, L., Creem-Regehr, S. H., & Thompson, W. T. (2019) · doi:10.31234/osf.io/5tg9y · Cited by 8 · see citing works
Interact: A visual what-if analysis tool for virtual product design
Ciorna, V., Mélançon, G., Petry, F., & Ghoniem, M. (2023) · Information Visualization · doi:10.1177/14738716231216030 · Cited by 7 · see citing works
Graph schema and best graph type to compare discrete groups: Bar, line, and pie
Zhao, F., & Gaschler, R. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.991420 · Cited by 7 · see citing works
Wilmer, J. B., & Kerns, S. H. (2022) · doi:10.31219/osf.io/av5ey · Cited by 7 · see citing works
Bars, lines and points: The effect of graph format on judgmental forecasting
Reimers, S. J., & Harvey, N. (2023) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2022.11.003 · Cited by 6 · see citing works
Data visualisation using R, for researchers who don't use RPreprint
Nordmann, E., McAleer, P., Toivo, W., Paterson, H., & DeBruine, L. M. (2021) · doi:10.31234/osf.io/4huvw · Cited by 6 · see citing works
Visualizing Uncertainty to Promote Clinicians’ Understanding of Measurement Error
Frans, N., Hummelen, B., Albers, C. J., & Paap, M. C. S. (2023) · Assessment · doi:10.1177/10731911221147042 · Cited by 5 · see citing works
“…with a wide range of interpretation errors, including reinforcing categorical reasoning about probabilities (Helske et al., 2021; Levontin et al., 2020; Padilla et al., 2022), and misinterpreting the probability of values within the error bar (Levontin et al., 2020; Newman & Scholl, 2012).”
BackgroundVisualising Uncertainty for Open Learner Model Users.
Epp, C. D., Susan Bull, A., & Johnson, M. D. (2014) · University of Birmingham Research Portal (University of Birmingham) · Cited by 5 · see citing works
Practice Improves Performance of a 2D Uncertainty Integration Task Within and Across Visualizations
Kusumastuti, S. A., Pollard, K. A., Oiknine, A. H., Dalangin, B., Raber, T. R., & Files, B. T. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3173889 · Cited by 5 · see citing works
Confidence Intervals and the Within-the-Bar Bias
Pentoney, C. S., & Berger, D. E. (2016) · The American Statistician · doi:10.1080/00031305.2016.1141706 · Cited by 5 · see citing works
Best Graph Type to Compare Discrete Groups: Bar, Dot, and Tally
Zhao, F., & Gaschler, R. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.775721 · Cited by 5 · see citing works
Benoit, I. D., Miller, E. G., Mirabito, A. M., & Catlin, J. R. (2022) · Health Marketing Quarterly · doi:10.1080/07359683.2022.2094101 · Cited by 5 · see citing works
Branson, Z., Parra, M. P., & Yurko, R. (2025) · Journal of Statistics and Data Science Education · doi:10.1080/26939169.2025.2537049 · Cited by 5 · see citing works
Gok, S., & Goldstone, R. L. (2024) · Cognitive Research · doi:10.1186/s41235-024-00561-x · Cited by 4 · see citing works
Wiebels, K., & Moreau, D. (2023) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459231160103 · Cited by 4 · see citing works
Matzen, L. E., Howell, B. C., Tuft, M., & Divis, K. (2024) · Computer Graphics Forum · doi:10.1111/cgf.15094 · Cited by 4 · see citing works
Morris, P. E., & Fritz, C. O. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1267-y · Cited by 4 · see citing works
Interactive Input and Visualization for Planning with Temporal Uncertainty
Höhn, M., Wunderlich, M., Ballweg, K., Kohlhammer, J., & von Landesberger, T. (2023) · SN Computer Science · doi:10.1007/s42979-023-01705-4 · Cited by 3 · see citing works
Antar, A. D., Molaei, S., Chen, Y.-Y., Lee, M. L., & Banovic, N. (2024) · ACM Symposium on User Interface Software and Technology · doi:10.1145/3654777.3676323 · Cited by 8 · see citing works
Estimating Bar Graph Averages: Overcoming Within-the-Bar Bias
Kang, H., Ji, J., Yun, Y., & Han, K.-H. (2021) · i-Perception · doi:10.1177/2041669520987254 · Cited by 3 · see citing works
Noa-Yarasca, E., Babbar-Sebens, M., Chiou, M., & Macuga, K. L. (2025) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434251324537 · Cited by 2 · see citing works
Dynamite plots in surgical research over 10 years: a meta-study using machine-learning analysis
Doggett, T. J., & Way, C. (2024) · Postgraduate Medical Journal · doi:10.1093/postmj/qgad134 · Cited by 2 · see citing works
Gesture and Language Production in Communication through Bar Graphs
Alaçam, Ö., Habel, C. U., & Acartürk, C. (2013) · eScholarship (California Digital Library) · Cited by 2 · see citing works
Xie, G., Du, W., Yuan, H., & Jiang, Y. (2021) · Sustainability · doi:10.3390/su13020441 · Cited by 2 · see citing works
Matzen, L. E., Rogers, A., & Howell, B. C. (2023) · Springer eBooks · doi:10.1007/978-3-031-34738-2_14 · Cited by 1 · see citing works
Understanding patterns of accumulation: Improving forecast-based decisions via nudging
Boz-Yılmaz, H. Z., & Boduroğlu, A. (2024) · Memory & Cognition · doi:10.3758/s13421-024-01519-6 · Cited by 1 · see citing works
Boronow, K. E., Maruzzo, A. J., Morello-Frosch, R. A., Patil, A. N., DeMicco, E., Brown, P., Padula, A. M., Geiger, S. D., & Brody, J. G. (2026) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/s41370-026-00840-3 · Cited by 1 · see citing works
plot2groups: an R package to plot scatter points for two groups of values
Xu, Y. J., Zhang, F., Wang, G., Cao, H., Cheng, Z., & Shugart, Y. Y. (2014) · Source Code for Biology and Medicine · doi:10.1186/1751-0473-9-23 · Cited by 1 · see citing works
Visualizing health inequality data: guidance for selecting and designing graphs and maps
Bergen, N., Kirkby, K., Nambiar, D., Schlotheuber, A., & Hosseinpoor, A. R. (2025) · International Journal for Equity in Health · doi:10.1186/s12939-025-02667-0 · Cited by 1 · see citing works
Matzen, L. E., Divis, K., Haass, M. J., & Cronin, D. A. (2020) · doi:10.31219/osf.io/9c83r · Cited by 1 · see citing works
An illusion of predictability in scientific resultsPreprint
Zhang, S., Heck, P. R., Meyer, M. N., Chabris, C. F., Goldstein, D. G., & Hofman, J. M. (2022) · doi:10.31235/osf.io/5tcgs · Cited by 1 · see citing works
Wiebels, K., & Moreau, D. (2021) · doi:10.31234/osf.io/uatyw · Cited by 1 · see citing works
The visualisation of data in a digital context
Ellis, D. A., & Merdian, H. L. (2018) · Routledge eBooks · doi:10.4324/9781315561622-9
Shalev, E., & Péer, E. (2023) · Journal of Behavioral Decision Making · doi:10.1002/bdm.2339
Understanding and Enhancing JSON-based DSL Interfaces for VisualizationPreprint
McNutt, A. M. (2023) · doi:10.31219/osf.io/fy246
Design and evaluation of the user experience in Human-Data Interaction
Victorelli, E. Z. (2021) · doi:10.47749/t/unicamp.2021.1164217
Multi-state Visualizations of Descriptive Statistics
Wang, X., & Hornbæk, K. (2024) · International Conference on Advanced Visual Interfaces (AVI) · doi:10.1145/3656650.3656662
Altukhi, Z. M., & Aljohani, N. F. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-62277-9_11
Learning Analytics in Reading Comprehension
Bustos-López, M., Machorro-Cano, I., Alor‐Hernández, G., Hernández-Capistrán, J., & Olmedo-Aguirre, J. O. (2024) · Intelligent systems reference library · doi:10.1007/978-3-031-66731-2_14
DECISIONS, VARIABILITY, AND VISUALIZATION: A NOVEL INSTRUMENT FOR DECISION MAKING UNDER VARIABILITYPreprint
del Rosario, Z., Saur, E., Jin, Lin, J., & Wilmer, J. B. (2025) · doi:10.31234/osf.io/vujta_v1
Beyond the Bar: Judging Dot Means on Bar Graphs – Dot Distribution Matters
Cui, L., Qian, X., & Liu, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93819-1_2
Bainbridge, W. A. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009343602.006
Dryhurst, S., & Freeman, A. L. J. (2024) · Apollo (University of Cambridge) · doi:10.57874/9vn0-jg91
The Bar-Tip Limit Error in Bar Charts: Exploring Its Relationship to the Within-the-Bar Bias
Reimann, D. (2026) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2026.3675047
“Showing the data” in published biology research
Freeman, B. G., Haldopoulos, M. E., Srinivasan, S., Cooper, T. L., An, S., Yasumuro, M., Kumar, A. V. S., Modarressi, M., Zhang, X., & Chopra, A. (2026) · mBio · doi:10.1128/mbio.00572-26
Data Visualization within and Beyond Psychological Science
Ellis, D., & Tang, J. (2026) · doi:10.1093/9780197859155.003.0334
The Influence of Between- and Within-Map Lightness Variation on Decision Making With Hazard Maps
Zimnicki, C., & Matzen, L. E. (2026) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2026.3713087
- Authors
- Newman, G. E., & Scholl, B. J.
- Affiliations
- Yale University (US)
- Corresponding author
- Newman, George E.
- Venue
- Psychonomic Bulletin & Review
- Published
- 2012-05-31
- Volume
- 19
- Issue
- 4
- Pages
- 601-607
- ISSN
- 1069-9384 (ISSN-L)
- 1531-5320
- OpenAlex
- W2092922484
- Cited by
- 101
- References
- 19
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 9 · 2023: 14 · 2022: 13 · 2021: 15 · 2020: 2 · 2019: 8 · 2018: 9 · 2017: 5 · 2016: 3 · 2015: 6 · 2014: 5 · 2013: 1
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Psychonomic Bulletin & Review (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Aesthetic Perception and Analysis
- Keywords
- bar charts, measures of central tendency, error bars, object perception, probability judgment, data visualization, visual perception, decision-making
- MeSH terms
- Adult, Attention, Decision Making, Humans, Pattern Recognition, Visual, Statistics as Topic
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 ›
Are you an author, or do you see a problem with this page? Request a review
