Meta-analysis of the effect of natural frequencies on Bayesian reasoning.

McDowell, Michelle; Jacobs, Perke

doi:10.1037/bul0000126

Cited by 104 publications

(184 citation statements)

References 136 publications

(426 reference statements)

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“…While experts from climate and environmental sciences may prefer to communicate GHG emission reductions in grams, others may find numerical format to be abstract, complex, and unfamiliar. Such communications may be further simplified by providing consumers with a single GHG emission value that they can use for comparison (Galesic et al 2016, McDowell andJacobs 2017), such as the GHG emissions associated with a medium-sized tomato (Camilleri et al 2019). Also, numerical formats like GHG emissions per calorie or average portion size (Camilleri et al 2019) might make communications about food-related GHG emissions easier to understand.…”

Section: Discussionmentioning

confidence: 99%

Public perceptions of how to reduce carbon footprints of consumer food choices

Kause

Bruin

Millward-Hopkins

et al. 2019

Environ. Res. Lett.

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Carbon footprints-the greenhouse gas (GHG) emissions associated with consumer food choicessubstantially contribute to climate change. Life cycle analyses from climate and environmental sciences have identified effective rules for reducing these food-related GHG emissions, including eating seasonal produce and replacing dairy and red meat with plant-based products. In a national UK survey, we studied how many and which rules our participants generated for reducing GHG emissions of produce, dairy, and protein-rich products. We also asked participants to estimate GHG emission reductions associated with pre-selected rules, expressed in either grams or percentages. We found that participants generated few and relatively less effective rules, including ambiguous ones like 'Buy local'. Furthermore, participants' numerical estimates of pre-selected rules were less accurate when they assessed GHG emission reductions in grams rather than in percentages. Findings suggest a need for communicating fewer rules in percentages, for informing consumers about reducing food-related GHG emissions.

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Section: Discussionmentioning

confidence: 99%

Public perceptions of how to reduce carbon footprints of consumer food choices

Kause

Bruin

Millward-Hopkins

et al. 2019

Environ. Res. Lett.

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“…Explain probabilities . Conveying probabilities as relative frequencies instead of single‐event frequencies has been shown to help people comprehend uncertainty (Gigerenzer and Edwards, ; McDowell and Jacobs, ; but see Joslyn and Nichols, 2009 for the opposite finding in the weather domain). Descriptions in relative frequencies force the communicator to specify the reference class: “In 30 out of 100 days with a forecast like this , ...” rather than “The probability of X is 30%” (Murphy et al .…”

Section: Three Diverse Practical Applicationsmentioning

confidence: 99%

Promoting the use of probabilistic weather forecasts through a dialogue between scientists, developers and end‐users

Fundel

Fleischhut

Herzog

et al. 2019

Quart J Royal Meteoro Soc

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Today's ensemble weather prediction systems provide reliable and sharp probabilistic forecasts-yet they are still rarely communicated to outside users because of two main worries: the difficulty of communicating probabilities to lay audiences and their presumed reluctance to use probabilistic forecasts. To bridge the gap between the forecasts available and their use in day-to-day decision making, we encourage scientists, developers, and end-users to engage in interdisciplinary collaborations. Here, we discuss our experience with three different approaches of introducing probabilistic forecasts to different user groups and the theoretical and practical challenges that emerged. The approaches range from quantitative analyses of users' revealed preferences online to a participatory developer-user dialogue based on trial cases and interactive demonstration tools. The examples illustrate three key points. First, to make informed decisions, users need access to probabilistic forecasts. Second, forecast uncertainty can be understood if its visual representations follow validated best practices from risk communication and information design; we highlight five important recommendations from that literature for communicating probabilistic forecasts. Third, to appreciate the value of probabilistic forecasts for their decisions, users need the opportunity to experience them in their everyday practice. With these insights and practical pointers, we hope to support future efforts to integrate probabilistic forecasts into everyday decision making. KEYWORDS decisions under uncertainty, probabilistic forecast, risk communication, user dialogue, weather 1This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…An important goal for future research is to explore how the bars technique can be integrated with other effective instructional techniques discovered by past research (Kurzenhäuser & Hoffrage, ; Ruscio, ; Sedlmeier & Gigerenzer, ; Sirota, Kostovičová, & Vallée‐Tourangeau, ). Perhaps the clearest conclusion one can draw from the Bayesian literature is that people perform better when problems are presented as nested frequencies instead of probabilities (see McDowell & Jacobs, , for a recent review), and using frequency‐based examples during instruction substantially improves learning outcomes in comparison to training on Bayes theorem with probability‐format problems (Kurzenhäuser & Hoffrage, ; Ruscio, ; Sedlmeier & Gigerenzer, ). Fortunately, the bars technique can be easily translated to frequency‐format problems with no essential change in the solution method.…”

Section: Discussionmentioning

confidence: 99%

A visualization technique for Bayesian reasoning

Starns

Cohen

Bosco

et al. 2018

Applied Cognitive Psychology

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We tested a method for solving Bayesian reasoning problems in terms of spatial relations as opposed to mathematical equations. Participants completed Bayesian problems in which they were given a prior probability and two conditional probabilities and were asked to report the posterior odds. After a pretraining phase in which participants completed problems with no instruction or external support, participants watched a video describing a visualization technique that used the length of bars to represent the probabilities provided in the problem. Participants then completed more problems with a chance to implement the technique by clicking interactive bars on the computer screen. Performance improved dramatically from the pretraining phase to the interactive-bar phase. Participants maintained improved performance in transfer phases in which they were required to implement the visualization technique with either pencil-and-paper or no external medium. Accuracy levels for participants using the visualization technique were very similar to participants trained to solve the Bayes theorem equation. The results showed no evidence of learning across problems in the pretraining phase or for control participants who did not receive training, so the improved performance of participants using the visualization method could be uniquely attributed to the method itself. A classroom sample demonstrated that these benefits extend to instructional settings. The results show that people can quickly learn to perform Bayesian reasoning without using mathematical equations.We discuss ways that a spatial solution method can enhance classroom instruction on Bayesian inference and help students apply Bayesian reasoning in everyday settings.

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Meta-analysis of the effect of natural frequencies on Bayesian reasoning.

Cited by 104 publications

References 136 publications

Public perceptions of how to reduce carbon footprints of consumer food choices

Public perceptions of how to reduce carbon footprints of consumer food choices

Promoting the use of probabilistic weather forecasts through a dialogue between scientists, developers and end‐users

A visualization technique for Bayesian reasoning

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