This paper 1) provides reasons why graphics should be effective aids to communicate risk; 2) reviews the use of visuals, especially graphical displays, to communicate risk; 3) discusses issues to consider when designing graphs to communicate risk; and 4) provides suggestions for future research. Key articles and materials were obtained from MEDLINE® and PsychInfo® databases, from reference article citations, and from discussion with experts in risk communication. Research has been devoted primarily to communicating risk magnitudes. Among the various graphical displays, the risk ladder appears to be a promising tool for communicating absolute and relative risks. Preliminary evidence suggests that people understand risk information presented in histograms and pie charts. Areas that need further attention include 1) applying theoretical models to the visual communication of risk, 2) testing which graphical displays can be applied best to different risk communication tasks (e.g., which graphs best convey absolute or relative risks), 3) communicating risk uncertainty, and 4) testing whether the lay public's perceptions and understanding of risk varies by graphical format and whether the addition of graphical displays improves comprehension substantially beyond numerical or narrative translations of risk and, if so, by how much. There is a need to ascertain the extent to which graphics and other visuals enhance the public's understanding of disease risk to facilitate decision-making and behavioral change processes. Nine suggestions are provided to help achieve these ends.

When participants make part-whole proportion judgments, systematic bias is commonly observed. In some studies, small proportions are overestimated and large proportions underestimated; in other studies, the reverse pattern occurs. Sometimes the bias pattern repeats cyclically with a higher frequency (e.g., overestimation of proportions less than .25 and between .5 and .75; underestimation otherwise). To account for the various bias patterns, a cyclical power model was derived from Stevens' power law. The model proposes that the amplitude of the bias pattern is determined by the Stevens exponent, beta (i.e., the stimulus continuum being judged), and that the frequency of the pattern is determined by a choice of intermediate reference points in the stimulus. When beta < 1, an over-then-under pattern is predicted; when beta > 1, the under-then-over pattern is predicted. Two experiments confirming the model's assumptions are described. A mixed-cycle version of the model is also proposed that predicts observed asymmetries in bias patterns when the set of reference points varies across trials.

Since the publication of Loftus and Masson's (1994) method for computing confidence intervals (CIs) in repeated-measures (RM) designs, there has been uncertainty about how to apply it to particular effects in complex factorial designs. Masson and Loftus (2003) proposed that RM CIs for factorial designs be based on number of observations rather than number of participants. However, determining the correct number of observations for a particular effect can be complicated, given the variety of effects occurring in factorial designs. In this paper the authors define a general "number of observations" principle, explain why it obtains, and provide step-by-step instructions for constructing CIs for various effect types. The authors illustrate these procedures with numerical examples.

The findings can be applied to the design of information displays for individual CID systems and soldier training.

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This article provides guidelines for presenting quantitative data in papers for publication. The article begins with a reader-centered design philosophy that distills the maxim “know thy user” into three components: (a) know your users′ tasks, (b) know the operations supported by your displays, and (c) match user's operations to the ones supported by your display. Next, factors affecting the decision to present data in text, tables, or graphs are described: the amount of data, the readers′ informational needs, and the value of visualizing the data. The remainder of the article outlines the design decisions required once an author has selected graphs as the data presentation medium. Decisions about the type of graph depend on the readers′ experience and informational needs as well as characteristics of the independent (predictor) variables and the dependent (criterion) variable. Finally, specific guidelines for the design of graphs are presented. The guidelines were derived from empirical studies, analyses of graph readers′ tasks, and practice-based design guidelines. The guidelines focus on matching the specific sensory, perceptual, and cognitive operations required to read a graph to the operations that the graph supports.

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