Graphs do not lead people to infer causation from correlation.

Abstract

Media articles often communicate the latest scientific findings, and readers must evaluate the evidence and consider its potential implications. Prior work has found that the inclusion of graphs makes messages about scientific data more persuasive (Tal & Wansink, 2016). One explanation for this finding is that such visualizations evoke the notion of “science”; however, results are mixed. In the current investigation we extend this work by examining whether graphs lead people to erroneously infer causation from correlational data. In two experiments we gave participants realistic online news articles in which they were asked to evaluate the research and apply the work’s findings to a real-life hypothetical scenario. Participants were assigned to read the text of the article alone or with an accompanying line or bar graph. We found no evidence that the presence of graphs affected participants’ evaluations of correlational data as causal. Given that these findings were unexpected, we attempted to directly replicate a well-cited article making the claim that graphs are persuasive (Tal & Wansink, 2016), but we were unsuccessful. Overall, our results suggest that the mere presence of graphs does not necessarily increase the likelihood that one infers incorrect causal claims.

Icon Arrays Reduce Concern Over COVID-19 Vaccine Side Effects: A Randomized Control Study

“…Some prior work has found that other types of data visualization, such as bar graphs (Tal & Wansink, 2016), increase the perceived credibility of data. However, more recent work has cast doubt on the validity of these findings (see Dragicevic & Jansen, 2017;Fansher et al, 2022). Future work could explore if including icon arrays influences the perceived trustworthiness of data.…”

Visual displays for communicating scientific uncertainty in influenza forecasts

“…We extend the natural correspondence principles to examine how congruence between four displays and four decisions affects people's (a) comprehension, (b) confidence in their decisions, and (c) ratings of display helpfulness. Many studies have examined graph comprehension in terms of inferences drawn from main effects (Shah and Freedman, 2011) or the interpretation of causal and correlational relationships (Fansher et al, 2022). Here, we examine comprehension in terms of understanding uncertainties, as reflected in participants' interpretation, utilization, and evaluation of displays, in the message assembly and interrogation aspects of graph comprehension (Pinker, 1990).…”