Perception of bar graphs – A biased impression?

Godau, Claudia; Vogelgesang, Tom; Gaschler, Robert

doi:10.1016/j.chb.2016.01.036

Cited by 18 publications

(24 citation statements)

References 21 publications

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“…Relatedly, Peebles (2008) demonstrated that people presented with bar graphs underestimated the distance of target values to the average (represented by a horizontal line parallel to the x axis). More recently, Godau et al (2016) documented converging evidence that people systematically underestimate mean values in graphs with rising bars, independently of the height of bars. Theoretically, visual attention is drawn to the length of bars, which are identified as objects attached to the x axis.…”

Section: Discussionmentioning

confidence: 96%

“…Such methods could also help to map the mechanisms underlying the debiasing effects of dot plots. For instance, eye-tracking methodology could be used to determine whether the dots attract people's attention to a larger extent than the space between dots and the x axis (Godau et al, 2016), and the extent to which any attentional differences affect interpretations. Process tracing methods could also help to understand how people perceive and interpret error bars, as well as their relative effectiveness in different contexts (or lack thereof), for different viewers.…”

Section: Discussionmentioning

confidence: 99%

“…Newman and Scholl (2012) also noted that the use of points to represent means instead of asymmetric bars could improve the accuracy of graph interpretations. Dots do not need to be connected to the x axis, and they may attract people’s attention to a larger extent than the space between the dots and the axis (Godau, Vogelgesang, & Gaschler, 2016). Thus, this kind of display should be less likely to trigger systematic biases in people’s judgements of the likelihood of different data points.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Biasing and debiasing health decisions with bar graphs: Costs and benefits of graph literacy

Okan

García‐Retamero

Cokely

et al. 2018

Quarterly Journal of Experimental Psychology

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Bar graphs can improve risk communication in medicine and health. Unfortunately, recent research has revealed that bar graphs are associated with a robust bias that can lead to systematic judgment and decision making errors. When people view bar graphs representing means they tend to believe that data points located within bars are more likely to be part of the underlying distributions than equidistant points outside bars. In three experiments we investigated potential consequences, key cognitive mechanisms, and generalizability of the within-the-bar bias in the medical domain. We also investigated the effectiveness of different interventions to reduce the effect of this bias and protect people from errors. Results revealed that the within-the-bar bias systematically affected participants' judgments and decisions concerning treatments for controlling blood glucose, as well as their interpretations of ecological graphs designed to guide health policy decisions. Interestingly, individuals with higher graph literacy showed the largest biases. However, the use of dot plots to replace bars improved the accuracy of interpretations. Perceptual mechanisms underlying the within-thebar bias and prescriptive implications for graph design are discussed.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Biasing and debiasing health decisions with bar graphs: Costs and benefits of graph literacy

Okan

García‐Retamero

Cokely

et al. 2018

Quarterly Journal of Experimental Psychology

View full text Add to dashboard Cite

show abstract

“…If the encoded subset is not adequate to solve a conceptual question, it could be extracted again by looking at the graph all at once. In the experiment of Godau et al. (2016), bias did not occur when using a point graph but did occur with a bar graph.…”

mentioning

confidence: 88%

“…The first affects people’s estimation of the data distribution when a single bar with mean values is presented (Figure 1; Newman & Scholl, 2012). The other occurs when people estimate the average point of several bars that are presented simultaneously (Godau et al., 2016). The two types are similar in terms of bias into the bar but have different characteristics.…”

mentioning

confidence: 99%

Estimating Bar Graph Averages: Overcoming Within-the-Bar Bias

Kang

Yun

et al. 2021

i-Perception

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Although most people are not aware of it, bias can occur when interpreting graphs. Within-the-bar bias describes a misinterpretation of the distribution of data underlying bar graphs that indicate an average or where the average estimation point moves inside the bar when the average of several graphs is estimated. This study proposes and tests two methods based on information processing to reduce within-the-bar bias. The first method facilitates bottom-up processing by changing various graph features, such as presenting confidence intervals, placing boundaries around the graph, and showing cumulative bars with different tones. The second method facilitates top-down processing by instructing participants to estimate the mean based on a dot at the end of each bar. Testing of the first method showed that cumulative bars reduced bias, but the other methods did not. The second method was found to reduce bias. Overall, our results demonstrate that the accurate interpretation of bar graphs can be facilitated through the manipulation of specific graph features and instruction.

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