Jan 17

Review of Albert and Rossman

Review of Albert and Rossman "Workshop Statistics: Discovery with Data, A Bayesian Approach", Key College Publishing, 2001

Who is this book for?

In this review I concentrate on how this book is similar to and different from  Agresti and Franklin. The book contains almost no formulas and in this respect is even more basic than Agresti and Franklin. The emphasis of the book is on the Bayesian approach, which is not mainstream Statistics, and this makes it stand out from the crowd. The advantages of this emphasis are described on p.11 (avoiding the notion of a sampling distribution, making the course shorter and doing with less recipes).

What I like

The text is business-like. Just one page (p.1) explains the difference between descriptive and inferential statistics, without even mentioning these names.

The book urges the instructor to reduce the amount of lecturing and rely more on active learning. It often prompts the student to think about ideas before providing the theoretical answer. That's what I like to do in my class. Activity 3-12 (Wrong Conclusions) pursues the same purpose.

The description of basic features of a data distribution on p.22 is concise and clear.

What I don't like

The definition of a categorical variable (p.5) does not allow one to distinguish it from a numerical one. See my explanation.

No attempt is made to improve students' algebra skills.  This is what undermines the attempt to explain the Bayesian approach.

Like Agresti and Franklin, the authors make the study of regression dependent on the correlation coefficient. See Correlation and regression are two separate entities.

The logical sequence is broken. In particular, probabilities are introduced after regression.

The normal distribution, one of the pillars of Statistics, is given too late (in Chapter 18).


As much as I like the idea of active learning, I cannot recommend the book, for the simple reason that it doesn't comply with the College Board curriculum.

Not being a Bayesian specialist, I was hoping to pick up something useful for myself. That hope didn't realize. The five chapters on the Bayesian approach are nothing more than just a collection of recipes accompanied by numerical examples. Even the Bayes theorem is not stated. If I were to write such a book, I would write it as a complement to a widely adopted text. This would allow me to avoid repeating the common stuff (graphical illustration of statistical data, measures of center and spread, probabilities etc.) and give more theory.