4 preregistered studies show that beauty increases trust in graphs from scientific papers, news, and social media.
Scientists, policymakers, and the public increasingly rely on data visualizations—such as COVID tracking charts, weather forecast maps, and political polling graphs—to inform important decisions. The esthetic decisions of graph-makers may produce graphs of varying visual appeal, independent of data quality.
Here we tested whether the beauty of a graph influences how much people trust it. Across 3 studies, we sampled graphs from social media, news reports, and scientific publications, and consistently found that graph beauty predicted trust. In a 4th study, we manipulated both the graph beauty and misleadingness.
We found that beauty, but not actual misleadingness, causally affected trust.
These findings reveal a source of bias in the interpretation of quantitative data and indicate the importance of promoting data literacy in education. [Particularly worrisome given how effective statistics design is ignored by designers optimizing only for beauty.]
[Keywords: esthetics, beauty-is-good stereotype/halo effect, causal effects, data visualizations, publication bias, public trust]
…Here we test the hypothesis that the beauty of data visualizations influences how much people trust them. We first examined the correlation between perceived beauty and trust in graphs. To maximize the generalizability and external validity of our findings, we systematically sampled graphs (Figure 1) of diverse types and topics (Figure 2) from the real world. These graphs spanned a wide range of domains, including social media (Study 1), news reports (Study 2), and scientific publications (Study 3). We asked participants how beautiful they thought the graphs looked and how much they trusted the graphs. We also measured how much participants found the graphs interesting, understandable, surprising, and negative, to control for potential confounds (Figure 3A). In addition to predicting trust ratings, we also examined whether participants’ beauty ratings predicted real-world impact. We measured impact using indices including the number of comments the graphs received on social media, and the number of citations the graphs’ associated papers had. Finally, we tested the causal effect of graph beauty on trust by generating graphs using arbitrary data (Study 4). We orthogonally manipulated both the beauty and the actual misleadingness of these graphs and measured how these manipulations affected trust.
…Results: Beauty correlates with trust across domains. We found that participants’ trust in graphs was associated with how beautiful participants thought the graphs looked for graphs across all 3 domains (Figure 3B): social media posts on Reddit (Pearson’s r = 0.45, p = 4.15×10−127 in Study 1a; r = 0.41, p = 3.28×10−231 in Study 1b), news reports (r = 0.43, p = 1.14×10−278 in Study 2), and scientific papers (r = 0.41, p = 6.×10−234 in Study 3). These findings indicate that, across diverse contents and sources of the graphs, perceived beauty and trust in graphs are reliably correlated in the minds of perceivers. The association between beauty and trust remained robust when controlling for factors that might influence both perceived beauty and trust, including how much participants thought the graphs were interesting, understandable, surprising, and negative (linear mixed modeling: b = 0.19, standardized 𝛽 = 0.22, p = 1.05×10−30 in Study 1a; β = 0.14, 𝛽 = 0.16, p = 8.81×10−46 in Study 1b; β = 0.14, 𝛽 = 0.15, p = 5.35×10−35 in Study 2; β = 0.10, 𝛽 = 0.12, p = 1.85×10−25 in Study 3; see Figure 1: for the coefficients of covariates). These findings indicate that beautiful visualizations predict increased trust even when controlling for the effects of interesting topics, understandable presentation, confirmation bias, and negativity bias.
Beauty predicts real-world popularity: We found that the real-world popularity of the graphs was associated with how beautiful participants thought they were. The more beautiful graphs from Reddit were associated with higher numbers of comments in both Study 1a (β = 0.04, 𝛽 = 0.04, p = 0.011) and Study 1b (β = 0.11, 𝛽 = 0.12, p = 2.84×10−22). The more beautiful graphs from scientific journals were associated with papers that had higher numbers of citations in Study 3 (β = 0.07, 𝛽 = 0.05, p = 0.001; but not higher numbers of views, β = 0.03, 𝛽 = 0.02, p = 0.264). The association between the perceived beauty of a paper’s graphs and the paper’s number of citations remained robust when controlling for the paper’s publication date and how much participants thought the graphs were interesting, understandable, surprising, and negative (β = 0.05, 𝛽 = 0.04, p = 0.005). These findings suggest that people’s bias in favor of trusting beautiful graphs has real-world consequences.
…Discussion: …A second, non-mutually exclusive, explanation suggests that this apparent bias may be rooted in rational thinking. More beautiful graphs may indicate that the data is of higher quality and that the graph maker is more skillful [2010, Beautiful Visualization: Looking at Data through the Eyes of Experts]. However, our results suggest that this reasoning may not be accurate. It does not require sophisticated techniques to make beautiful graphs: we reliably made graphs look more beautiful simply by increasing their resolution and color saturation, and using a legible, professional font (Figure 4A–B). Findings from the real-world graphs (Studies 1–3) also suggest that one could make a very basic graph such as a bar plot look very beautiful (Figure S2F). Visual inspection of the more and less beautiful real-world graphs suggests that people perceive graphs with more colors (eg. rainbow colors), shapes (eg. cartoons, abstract shapes), and meaningful text (eg. a title explaining the meaning of the graph) as more beautiful. It also does not require high quality data to make a beautiful graph either: we generated graphs that were perceived as beautiful using arbitrary data (Figure 4B).
Therefore, our findings highlight that the beauty of a graph may not be an informative cue for its quality. Even if beauty was correlated with actual data quality in the real-world, this would be a dangerous and fallible heuristic to rely upon for evaluating research and media.