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Improving Understanding of Health-Relevant Numerical Information

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Psychological Perspectives on Risk and Risk Analysis

Abstract

In this chapter, we discuss why risks are often not communicated in a transparent and understandable way and why this is problematic. At the core of the chapter are four examples that illustrate how risk communication can be improved. These examples are (a) the use of natural frequencies in the context of diagnostic reasoning, (b) the use of visual aids to support the beneficial effect of natural frequency representations, (c) the use of natural frequencies to clarify the distinction between relative and absolute risk reduction, and (d) a clarification of the meaning and pitfalls of survival rates that are often used to quantify the benefit of screening programs. In each of these topics, we describe original empirical studies illuminating a specific problem as well as how these problems can be overcome, and we discuss practical implications of the results and the proposed solutions. Subsequently, we illustrate, using an example from mammography screening, what transparent risk communication could look like. The chapter concludes with a discussion of training programs designed to enhance health-related, high-stakes decision making.

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Acknowledgments

The authors thank the editors of this book for helpful comments on an earlier version and the Swiss National Science Foundation (100014–140503/1) and the Ministerio de Economía y Competitividad (Spain) (PSI2011-22954 and PSI2014-51842-R) for financial support. Parts of this chapter are adopted from Hoffrage and Koller (2015).

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Authors and Affiliations

  1. Department of Organizational Behavior, Faculty of Business and Economics, University of Lausanne, Lausanne, Switzerland

    Ulrich Hoffrage

  2. Department of Experimental Psychology, University of Granada, Granada, Spain

    Rocio Garcia-Retamero

Authors
  1. Ulrich Hoffrage
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  2. Rocio Garcia-Retamero
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Corresponding author

Correspondence to Ulrich Hoffrage .

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Editors and Affiliations

  1. Agelab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Martina Raue

  2. Department of Experimental Psychology, University of Regensburg, Regensburg, Germany

    Eva Lermer

  3. Institute of Psychology, UMIT - Private University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria

    Bernhard Streicher

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Hoffrage, U., Garcia-Retamero, R. (2018). Improving Understanding of Health-Relevant Numerical Information. In: Raue, M., Lermer, E., Streicher, B. (eds) Psychological Perspectives on Risk and Risk Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-92478-6_12

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