Teaching Statistics A Bag of Tricks

Students in the sciences, economics, social sciences, and medicine take an introductory statistics course. And yet statistics can be notoriously difficult for instructors to teach and for students to learn. To help overcome these challenges, Gelman and Nolan have put together this fascinating and thought-provoking book. Based on years of teaching experience the book provides a wealth of demonstrations, activities, examples, and projects that involve active student participation. Part I of the book presents a large selection of activities for introductory statistics courses and has chapters such as 'First week of class'-- with exercises to break the ice and get students talking; then descriptive statistics, graphics, linear regression, data collection (sampling and experimentation), probability, inference, and statistical communication. Part II gives tips on what works and what doesn't, how to set up effective demonstrations, how to encourage students to participate in class and to work effectively in group projects. Course plans for introductory statistics, statistics for social scientists, and communication and graphics are provided. Part III presents material for more advanced courses on topics such as decision theory, Bayesian statistics, sampling, and data science.

1 - Introduction
2 - First week of class
3 - Descriptive statistics
4 - Statistical graphics
5 - Linear regression and correlation
6 - Data collection
7 - Statistical literacy and the news media
8 - Probability
9 - Statistical inference
10 - Multiple regression and nonlinear models
11 - Lying with statistics
12 - How to do it
13 - Structuring an introductory statistics course
14 - Teaching statistics to social scientists
15 - Statistics diaries
16 - A course in statistical communication and graphics
17 - Decision theory and Bayesian statistics
18 - Student activities in survey sampling
19 - Problems and projects in probability
20 - Directed projects in a mathematical statistics course
21 - Statistical thinking in a data science course

Andrew Gelman is Professor of Statistics and Professor of Political Science and Director of the Applied Sciences Center at Columbia University. He has published over 250 articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy. Deborah Nolan is Professor of Statistics at the University of California, Berkeley. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research.