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Fast and frugal trees serve as efficient heuristics for decision under risk. We describe the construction of fast and frugal trees and compare their robustness for prediction under risk with that of Bayesian networks. In particular, we analyze situations of risky decisions in the medical domain. We show that the performance of fast and frugal trees does not fall too far behind that of the more complex Bayesian networks.<\/jats:p>","DOI":"10.1007\/s11943-019-00259-3","type":"journal-article","created":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T12:02:41Z","timestamp":1574769761000},"page":"269-278","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Statistical literacy for classification under risk: an educational perspective"],"prefix":"10.1007","volume":"13","author":[{"given":"Laura","family":"Martignon","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3106-140X","authenticated-orcid":false,"given":"Kathryn","family":"Laskey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,21]]},"reference":[{"key":"259_CR1","volume-title":"UCI machine learning repository. 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