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How to improve people's interpretation of probabilities of precipitation

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  • Marie Juanchich
  • Miroslav Sirota

Abstract

Most research into uncertainty focuses on how people estimate probability magnitude. By contrast, this paper focuses on how people interpret the concept of probability and why they often misinterpret it. In a weather forecast context, we hypothesised that the absence of an explicit reference class and the polysemy of the percentage format are causing incorrect probability interpretations, and test two interventions to help people make better probability interpretation. In two studies ( N = 1337), we demonstrate that most people from the UK and the US do not interpret probabilities of precipitation correctly. The explicit mention of the reference class helped people to interpret probabilities of precipitation better when the target area was explicit; but this was not the case when it was not specified. Furthermore, the polysemy of the percentage format is not likely to cause these misinterpretations, since a non-polysemous format (e.g. verbal probability) did not facilitate a correct probability interpretation in our studies. A Bayes factor analysis supported both of these conclusions. We discuss theoretical and applied implications of our findings.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:jriskr:v:19:y:2016:i:3:p:388-404
    DOI: 10.1080/13669877.2014.983945
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    References listed on IDEAS

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    1. Riege, Anine H. & Teigen, Karl Halvor, 2013. "Additivity neglect in probability estimates: Effects of numeracy and response format," Organizational Behavior and Human Decision Processes, Elsevier, vol. 121(1), pages 41-52.
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    3. 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.
    4. Marcel A L M van Assen & Robbie C M van Aert & Michèle B Nuijten & Jelte M Wicherts, 2014. "Why Publishing Everything Is More Effective than Selective Publishing of Statistically Significant Results," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-5, January.
    5. David V. Budescu & Han-Hui Por & Stephen B. Broomell & Michael Smithson, 2014. "The interpretation of IPCC probabilistic statements around the world," Nature Climate Change, Nature, vol. 4(6), pages 508-512, June.
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    2. Lackes, Richard & Siepermann, Markus & Vetter, Georg, 2020. "What drives decision makers to follow or ignore forecasting tools - A game based analysis," Journal of Business Research, Elsevier, vol. 106(C), pages 315-322.

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