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“A 30% Chance of Rain Tomorrow”: How Does the Public Understand Probabilistic Weather Forecasts?

Author

Listed:
  • Gerd Gigerenzer
  • Ralph Hertwig
  • Eva Van Den Broek
  • Barbara Fasolo
  • Konstantinos V. Katsikopoulos

Abstract

The weather forecast says that there is a “30% chance of rain,” and we think we understand what it means. This quantitative statement is assumed to be unambiguous and to convey more information than does a qualitative statement like “It might rain tomorrow.” Because the forecast is expressed as a single‐event probability, however, it does not specify the class of events it refers to. Therefore, even numerical probabilities can be interpreted by members of the public in multiple, mutually contradictory ways. To find out whether the same statement about rain probability evokes various interpretations, we randomly surveyed pedestrians in five metropolises located in countries that have had different degrees of exposure to probabilistic forecasts––Amsterdam, Athens, Berlin, Milan, and New York. They were asked what a “30% chance of rain tomorrow” means both in a multiple‐choice and a free‐response format. Only in New York did a majority of them supply the standard meteorological interpretation, namely, that when the weather conditions are like today, in 3 out of 10 cases there will be (at least a trace of) rain the next day. In each of the European cities, this alternative was judged as the least appropriate. The preferred interpretation in Europe was that it will rain tomorrow “30% of the time,” followed by “in 30% of the area.” To improve risk communication with the public, experts need to specify the reference class, that is, the class of events to which a single‐event probability refers.

Suggested Citation

  • Gerd Gigerenzer & Ralph Hertwig & Eva Van Den Broek & Barbara Fasolo & Konstantinos V. Katsikopoulos, 2005. "“A 30% Chance of Rain Tomorrow”: How Does the Public Understand Probabilistic Weather Forecasts?," Risk Analysis, John Wiley & Sons, vol. 25(3), pages 623-629, June.
  • Handle: RePEc:wly:riskan:v:25:y:2005:i:3:p:623-629
    DOI: 10.1111/j.1539-6924.2005.00608.x
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    Cited by:

    1. Marie Juanchich & Miroslav Sirota, 2016. "How to improve people's interpretation of probabilities of precipitation," Journal of Risk Research, Taylor & Francis Journals, vol. 19(3), pages 388-404, March.
    2. Sven Gruener, 2024. "Determinants of Gullibility to Misinformation: A Study of Climate Change, COVID-19 and Artificial Intelligence," Journal of Interdisciplinary Economics, , vol. 36(1), pages 58-78, January.
    3. Dubard Barbosa, Saulo & Fayolle, Alain & Smith, Brett R., 2019. "Biased and overconfident, unbiased but going for it: How framing and anchoring affect the decision to start a new venture," Journal of Business Venturing, Elsevier, vol. 34(3), pages 528-557.
    4. Caroline M. Vass & Niall J. Davison & Geert Stichele & Katherine Payne, 2020. "A Picture is Worth a Thousand Words: The Role of Survey Training Materials in Stated-Preference Studies," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 13(2), pages 163-173, April.
    5. Ambika Markanday & Steffen Kallbekken & Ibon Galarraga, 2022. "The power of impact framing and experience for determining acceptable levels of climate change-induced flood risk: a lab experiment," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(2), pages 1-18, February.
    6. Theresa A. K. Knoblauch & Michael Stauffacher & Evelina Trutnevyte, 2018. "Communicating Low‐Probability High‐Consequence Risk, Uncertainty and Expert Confidence: Induced Seismicity of Deep Geothermal Energy and Shale Gas," Risk Analysis, John Wiley & Sons, vol. 38(4), pages 694-709, April.
    7. Vivianne H. M. Visschers & Ree M. Meertens & Wim W. F. Passchier & Nanne N. K. De Vries, 2009. "Probability Information in Risk Communication: A Review of the Research Literature," Risk Analysis, John Wiley & Sons, vol. 29(2), pages 267-287, February.
    8. Alan J. Card & James R. Ward & P. John Clarkson, 2014. "Trust‐Level Risk Evaluation and Risk Control Guidance in the NHS East of England," Risk Analysis, John Wiley & Sons, vol. 34(8), pages 1469-1481, August.
    9. Michael Siegrist & Carmen Keller, 2011. "Natural frequencies and Bayesian reasoning: the impact of formal education and problem context," Journal of Risk Research, Taylor & Francis Journals, vol. 14(9), pages 1039-1055, October.
    10. Saulo Dubard Barbosa & Alain Fayolle & Brett Smith, 2019. "Biased and overconfident, unbiased but going for it: How framing and anchoring affect the decision to start a new venture," Post-Print hal-01988083, HAL.
    11. Ihtisham A. Malik & Robert W. Faff & Kam F. Chan, 2020. "Market response of US equities to domestic natural disasters: industry‐based evidence," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3875-3904, December.
    12. Paolo Figini & Simona Cicognani & Lorenzo Zirulia, 2023. "Booking in the Rain. Testing the Impact of Public Information on Prices," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(3), pages 1329-1364, November.
    13. Gruener, Sven, 2021. "Susceptibility to misinformation: a study of climate change, Covid-19, and artificial intelligence," SocArXiv x8efq, Center for Open Science.
    14. Gruener, Sven, 2021. "Misinformation: determinants of gullibility," SocArXiv r3fx7, Center for Open Science.
    15. Caponecchia, Carlo & Tan, David T., 2019. "Exploring the traveller underinsurance problem," Annals of Tourism Research, Elsevier, vol. 76(C), pages 343-345.
    16. V.H.M. Visschers & P.M. Wiedemann & H. Gutscher & S. Kurzenhäuser & R. Seidl & C.G. Jardine & D.R.M. Timmermans, 2012. "Affect-inducing risk communication: current knowledge and future directions," Journal of Risk Research, Taylor & Francis Journals, vol. 15(3), pages 257-271, March.
    17. Christoph Werner & Tim Bedford & John Quigley, 2018. "Sequential Refined Partitioning for Probabilistic Dependence Assessment," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2683-2702, December.
    18. Astrid Kause & Wändi Bruine de Bruin & Fai Fung & Andrea Taylor & Jason Lowe, 2020. "Visualizations of Projected Rainfall Change in the United Kingdom: An Interview Study about User Perceptions," Sustainability, MDPI, vol. 12(7), pages 1-21, April.
    19. Carmen Keller & Michael Siegrist & Heinz Gutscher, 2006. "The Role of the Affect and Availability Heuristics in Risk Communication," Risk Analysis, John Wiley & Sons, vol. 26(3), pages 631-639, June.

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