Natural frequencies improve Bayesian reasoning in simple and complex inference tasks

Hoffrage, Ulrich and Krauss, Stefan and Martignon, Laura and Gigerenzer, Gerd (2015) Natural frequencies improve Bayesian reasoning in simple and complex inference tasks. FRONTIERS IN PSYCHOLOGY, 6: 1473. ISSN 1664-1078,

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Abstract

Representing statistical information in terms of natural frequencies rather than probabilities improves performance in Bayesian inference tasks. This beneficial effect of natural frequencies has been demonstrated in a variety of applied domains such as medicine, law, and education. Yet all the research and applications so far have been limited to situations where one dichotomous cue is used to infer which of two hypotheses is true. Real-life applications, however, often involve situations where cues (e.g., medical tests) have more than one value, where more than two hypotheses (e.g., diseases) are considered, or where more than one cue is available. In Study 1, we show that natural frequencies, compared to information stated in terms of probabilities, consistently increase the proportion of Bayesian inferences made by medical students in four conditions three cue values, three hypotheses, two cues, or three cues by an average of 37 percentage points. In Study 2, we show that teaching natural frequencies for simple tasks with one dichotomous cue and two hypotheses leads to a transfer of learning to complex tasks with three cue values and two cues, with a proportion of 40 and 81% correct inferences, respectively. Thus, natural frequencies facilitate Bayesian reasoning in a much broader class of situations than previously thought.

Item Type: Article
Uncontrolled Keywords: STATISTICAL INFORMATION; DIAGNOSTIC INFERENCES; FRUGAL HEURISTICS; REPRESENTATION; DECISIONS; JUDGMENT; INSTRUCTION; FACILITATE; FORMATS; CUE; Bayesian inference; representation of information; natural frequencies; task complexity; instruction; fast-and-frugal trees; visualization
Subjects: 500 Science > 510 Mathematics
Divisions: Mathematics > Prof. Dr. Stefan Krauss
Depositing User: Dr. Gernot Deinzer
Date Deposited: 06 Jun 2019 06:29
Last Modified: 06 Jun 2019 06:29
URI: https://pred.uni-regensburg.de/id/eprint/4617

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