The interpretation of a graph depends on the “viewer’s familiarity with the content depicted in a graph, and the viewer’s graphicacy skills” as well as the design of the graph (Shah and Freedman, 2009). For example, graph viewers are less likely to discern the relevant results from a graph if the data in the graph are not grouped to form appropriate “visual chunks” (Shah et al., 1999) or exhibited in a format that supports the intended inferences (Shah and Freedman, 2009). In addition, some research indicates that individuals differ in how they use the information in different presentations of data (Boduroglu and Shah, 2009), further complicating the use of graphical presentations.
There is also some evidence that graphic displays increase risk aversion. For example, one study that examined how well visual displays of risk communicated low-probability events found that adding graphics to numeric presentations increased participants’ willingness to pay for risk reductions (Stone et al., 1997). There is no correct level of perceived risk, however, so it is not possible to rank the effectiveness of various displays based on this outcome.
Furthermore, graphs can be designed—either intentionally or unintentionally—to call attention to certain aspects of a message and detract from others. Highlighting the foreground rather than the background can make people more risk averse. For example, people are more risk averse after seeing a bar graph that only shows the differences in the number of people suffering from serious gum disease with the denominator of “per 5,000” people included in the figure legend than after seeing a bar graph that depicts both differences in gum disease and the denominator of 5,000 people (Stone et al., 2003). Even when such foreground–background salience and gain–loss framing (see discussion below) are controlled, however, evidence indicates that graphic displays lead to greater risk aversion than numerical presentations (Slovic and Monahan, 1995; Slovic et al., 2000).
The ability to use interactive visualizations to display information and uncertainty about that information has increased with the evolution of computer technology. Spiegelhalter et al. (2011) point out that “increasing availability of online data and public interest in quantitative information has led to a golden age of infographics” (p. 1399), including the ability to create graphics with interactive features. Such interactive graphics have the potential to increase understanding and retention and to help counteract differences in numeracy, and this potential could be applied in the communication of uncertainty. Spiegelhater notes, however, that while there is huge potential applications and uses for infographics with interactive features, such graphics have not yet been evaluated empirically.
One limitation of most of those graphical presentations is that they display only one variable at a time. For example, they might show how the uncertainty in an estimate of human health risks varies among individuals