title: Transparency in risk communication: graphical and analog tools.
abstract: Why is it that the public can read and write but only a few understand statistical information? Why are elementary distinctions, such as that between absolute and relative risks, not better known? In the absence of statistical literacy, key democratic ideals, such as informed consent and shared decision making in health care, will remain science fiction. In this chapter, we deal with tools for transparency in risk communication. The focus is on graphical and analog representations of risk. Analog representations use a separate icon or sign for each individual in a population. Like numerical representations, some graphical forms are transparent, whereas others indiscernibly mislead the reader. We review cases of (1) tree diagrams for representing natural versus relative frequency, (2) decision trees for the representation of fast and frugal decision making, (3) bar graphs for representing absolute versus relative risk, (4) population diagrams for the analog representation of risk, and (5) a format of representation that employs colored tinker cubes for the encoding of information about individuals in a population. Graphs have long enjoyed the status of being "worth a thousand words" and hence of being more readily accessible to human understanding than long-winded symbolic representations. This is both true and false. Graphical tools can be just as well employed for transparent and nontransparent risk communications.
authors.affiliation: Institute of Mathematics and Computing, University of Education Ludwigsburg, Reuteallee 46, 71634 Ludwigsburg, Germany. kurzmilcke@ph-ludwigsburg.de
authors.forename: Elke
authors.initials: E
authors.lastname: Kurz-Milcke
authors.forename: Gerd
authors.initials: G
authors.lastname: Gigerenzer
authors.forename: Laura
authors.initials: L
authors.lastname: Martignon
mesh_terms: D005075:Biological Evolution; D001943:Breast Neoplasms / Q000175:diagnosis / Q000453:epidemiology; D003142:Communication; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D003657:Decision Making; D005260:Female; D015658:HIV Infections / Q000175:diagnosis* / Q000453:epidemiology; D006679:HIV Seropositivity / Q000175:diagnosis; D006801:Humans; D008403:Mass Screening; D008962:Models, Theoretical; D012306:Risk; D018570:Risk Assessment