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Teaching StatisticsA Bag of Tricks$
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Andrew Gelman and Deborah Nolan

Print publication date: 2017

Print ISBN-13: 9780198785699

Published to Oxford Scholarship Online: September 2017

DOI: 10.1093/oso/9780198785699.001.0001

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Decision theory and Bayesian statistics

Decision theory and Bayesian statistics

Chapter:
(p.277) 17 Decision theory and Bayesian statistics
Source:
Teaching Statistics
Author(s):

Andrew Gelman

Deborah Nolan

Publisher:
Oxford University Press
DOI:10.1093/oso/9780198785699.003.0017

This chapter outlines some of our more effective demonstrations for teaching decision theory and Bayesian statistics. Our contribution here is in the tricks used to involve students; the ideas behind most of the demonstrations are well known. The activities serve several purposes, including focusing student attention on difficult conceptual issues that are hard to learn in a lecture or by solving homework problems (e.g., the principle of expected gain and determining the value of a life); alerting students to their cognitive illusions (e.g., the incoherent utilities for money and uncalibrated subjective probability intervals); bringing personal issues into the class (e.g., different areas of knowledge in the subjective probability intervals and personal decision problems); dramatizing counterintuitive results which a student might not realize as counterintuitive; and demonstrating the multiple levels of uncertainty in a Bayesian analysis, as well as the coverage property of posterior intervals.

Keywords:   Calibration, coverage, decision problem, expected gain, posterior interval, probability interval, teaching statistics

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