Teaching Bayesian Reasoning in Less Than Two Hours Export

@article{SedlmeierGigerenzer2001, abstract = {The authors present and test a new method of teaching Bayesian reasoning, something about which previous teaching studies reported little success. Based on G. Gigerenzer and U. Hoffrage's (1995) ecological framework, the authors wrote a computerized tutorial program to train people to construct frequency representations (representation training) rather than to insert probabilities into Bayes' rule (rule training). Bayesian computations are simpler to perform with natural frequencies than with probabilities, and there are evolutionary reasons for assuming that cognitive algorithms have been developed to deal with natural frequencies. In 2 studies, the authors compared representation training with rule training; the criteria were an immediate learning effect, transfer to new problems, and long-term temporal stability. Rule training was as good in transfer as representation training, but representation training had a higher immediate learning effect and greater temporal stability.}, author = {Sedlmeier, Peter and Gigerenzer, Gerd}, citeulike-article-id = {3398398}, journal = {Journal of Experimental Psychology: General}, keywords = {bayes, bayesian, frequencies, literacy, natural, reasoning, statistical, theorem, turs-project}, number = {3}, pages = {380--400}, posted-at = {2008-10-12 16:27:14}, priority = {0}, title = {Teaching Bayesian Reasoning in Less Than Two Hours}, volume = {130}, year = {2001} }

TY - JOUR ID - SedlmeierGigerenzer2001 L3 - citeulike-article-id:3398398 N2 - The authors present and test a new method of teaching Bayesian reasoning, something about which previous teaching studies reported little success. Based on G. Gigerenzer and U. Hoffrage’s (1995) ecological framework, the authors wrote a computerized tutorial program to train people to construct frequency representations (representation training) rather than to insert probabilities into Bayes’ rule (rule training). Bayesian computations are simpler to perform with natural frequencies than with probabilities, and there are evolutionary reasons for assuming that cognitive algorithms have been developed to deal with natural frequencies. In 2 studies, the authors compared representation training with rule training; the criteria were an immediate learning effect, transfer to new problems, and long-term temporal stability. Rule training was as good in transfer as representation training, but representation training had a higher immediate learning effect and greater temporal stability. IS - 3 JF - Journal of Experimental Psychology: General EP - 400 TI - Teaching Bayesian Reasoning in Less Than Two Hours VL - 130 SP - 380 KW - bayes KW - bayesian KW - frequencies KW - literacy KW - natural KW - reasoning KW - statistical KW - theorem KW - turs-project AU - Sedlmeier, Peter AU - Gigerenzer, Gerd PY - 2001/// ER -