Notebooks

Teaching Statistics

27 Sep 2021 17:01

Doing this is now, officially, what I am paid for. I am basically unembarrassed about doing this while never having taken a statistics class --- after all, I do statistical research, so it's not exactly like a celibate man offering advice on marriage --- but I do want to do it better.

One thing which particularly concerns me is that almost all the introductory textbooks I run across seem like either cookbooks, or lower and distorted forms of Cramér's Mathematical Methods of Statistics. Cramér's book is great, but giving a debased version of it to engineers or social scientists doesn't seem all that effective. So I'm interested in good approaches to teaching statistics as a way of learning about the world from data, not a set of rituals or a calculational exercise in basic probability theory. If they do a good job of teaching about computer-intensive methods and applied probability, so much the better.

In fact, what I'd really like is for somebody to write a popular book on "better living through data analysis". I wish I could say that Freakonomics was that book, but it isn't.

Recommended, good examples:
• D. R. Cox and Christl A. Donnelly, Principles of Applied Statistics
• A. C. Davison, Statistical Modeling
• Larry Gonick and Woollcott Smith, The Cartoon Guide to Statistics
• D. Huff, How to Lie with Statistics
• Larry Wasserman, All of Statistics
Recommended second or secondary books (i.e., ones with too few technicalities to be self-contained, first-reading texts):
• Robert P. Abelson, Statistics as Principled Argument ["Author's note: There is a Robert P. Abelson who sings in the Yiddish theater in New York. Although theatrically inclined, I cannot (alas) claim to be that person also."]
• Richard A. Berk, Regression Analysis: A Constructive Critique
• Tim Hesterberg, "What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum", arxiv:1411.5279
Recommended, misc.:
• Nathan Moore, Nicole Schoolmeesters, "Computational Physics and Reality: Looking for Some Overlap at the Blacksmith Shop", arxiv:0904.3960 [This sounds like it might also work for a course in stochastics...]
• Murray Aitkin, Brian Francis, John Hinde and Ross Darnell, Statistical Modelling in R
• Benjamin M. Bolker, Ecological Models and Data in R
• F. M. Dekking, C. Kraaikamp, H. P. Lopuhaä and L. E. Meester, A Modern Introduction to Probability and Statistics: Understanding How and Why
• Finkelstein, Smith and Levin, Statistics for Lawyers ["Despite its pedestrian title, it is not a routine statistics text with legal examples tossed in. The selection of topics and examples, as well as the exposition of statistics and law, is erudite, informed, and even entertaining." --- or so says the review quoted by Springer Verlag]
• Andrew Gelman and and Deborah Nolan, Teaching Statistics: A Bag of Tricks
• Phillip I. Good, Resampling Methods: A Practical Guide to Data Analysis
• Phillip I. Good and James W. Hardin, Common Errors in Statistics (and How to Avoid Them)
• Daniel T. Kaplan, Statistical Modeling: A Fresh Approach [Thanks to Ben Hansen for the pointer]
• Dana K. Keller, The Tao of Statistics: A Path to Understanding (With No Math)
• Gary King, Robert O. Keohane and Sidney Verba, Designing Social Inquiry: Scientific Inference in Qualitative Research
• Ben Klemens, Modeling with Data [website with draft text. Looks interesting and I like the idea of integrating it with computing, and with databases.]
• Marsha Lovett, Oded Meyer and Candace Thille, "The Open Learning Initiative: Measuring the Effectiveness of the OLI Statistics Course in Accelerating Student Learning", Journal of Interactive Media in Education 2008:14
• Jane E. Miller, The Chicago Guide to Writing about Multivariate Analysis
• Neil J. Salking, Statistics for People Who (Think They) Hate Statistics
• David J. Saville and Graham R. Wood, Statistical Methods: The Geometric Approach [Thanks to David Weisman for the pointer]
• Jefferson Hane Weaver, Conquering Statistics: Numbers Without the Crunch