I listened to this on my afternoon commute. Dr. Ryan's discussion of essential questions resonates with me personally and professionally. I will be purchasing a hardcover edition of the book for reading and reference. I wish I had bought into the ask good questions idea a long, long time ago. ( )

Read this mostly b/c I like to learn about the original problems people were trying to solve when they came up with tools we use today, and what was the state of the art before that. I liked the choice of topics for each chapter (each of them does talk about something different and I have a better global view of the field than when I started). Highlights for me are the chapter on likelihood, the one on experiment design and the one on residuals.

I disliked the writing style: the interesting (eg. along the lines of Laplace was trying to do X, he drew this Figure) is intertwined with the pedantic (eg. He won a prize, he corresponded with X, he had an enmity with Y, he was a member of such and such society) at such small granularity that forces you to read it... I put it down for several weeks a couple of times. ( )

Why Seven? Stephen Stigler notes that the title of his Seven Pillars of Statistical Wisdom is borrowed from T.E. Lawrence's own Seven Pillars of Wisdom, and that both he and Lawrence of Arabia drew on Proverbs 9:1 as a source: "Wisdom hath built her house, she hath hewn out her seven pillars" (3). With this bow to tradition, Stigler goes on to note that an eighth pillar may well be forthcoming, without commenting on what it might be (203).

While we await this possible eighth pillar, the seven current pillars are: Aggregation, Information, Likelihood, Intercomparison, Regression, Design, and Residual. While the delineation is subjective, Stigler shows a strong grasp of the material by tracing the history of each of these "bins." He finds interesting things to state about each pillar (Aggregation "inherently involves the discarding of information, an act of 'creative destruction'", 196), but lacks fuller development.

There is a strong References section, but for a book published by an academic press, the footnotes are a little light (the 33 notes in the fifth chapter, "Regression," are an outlier). While the book is not dryly "academic," neither is it an introductory text; the reader needs a general understanding of numerous statistical concepts in order to grasp the subjectivity of Stigler's slicing. It thus occupies a sort of middle ground: too short and subjective for the specialist, yet a bit too specialized and obtuse for the lay reader. I would very much like to see a fuller treatment. ( )

An overview of the foundational concepts of modern statistics. I liked the way the author organized the topics, but I wish he had taken less of an historical approach and wrote more about the "pillars'" role in contemporary practice. Still, as a text on the antecedents of the field, this book is one of the best and has some surprising discoveries. It is well illustrated, too. ( )

The book is about seven themes of statistics (aggregation [means mostly], likelihood [n-root rule and exceptions], intercomparison [student t-test], regression [regression to the mean and its implications], design [randomization] and residual [residual plots and nested models]) . I'm not sure who the audience is supposed to be. It seems from organization around seven themes and the size of the book that the book is targeted towards a general audience. Some of the material supports this, the earlier material starts on averages and the emergence of the arithmetical mean from astronomy. However, the author occasionally lapses into highly technical work that I found difficult to follow (i.e. "In its simplest form in parametric estimation problems, the fisher information is the expected value of the square of the score function defined to be the derivative of the logarithm of the probability density function of the data") though there are a few interesting examples and historical facts (pyx, the origin of the student t-distribution and the cauchy distribution as an exception to the central limit theorem). Other times the author name drops a method as a solution to a technical problem without further explanation. Seems like the book would only make sense to someone very well versed in statistics, in which case, why would they be reading this book? I did like the explanation of galtons discovery of regression towards the mean, and the role of the Galton machine in relegating the rule of three into the historical dustbin. ( )