**W**hen Jean-Louis Foulley pointed out to me this page in the September issue of Amstat News, about nominating a favourite teacher, I told him it had to be an homonym statistician! Or a practical joke! After enquiry, it dawned on me that this completely underserved inclusion came from a former student in my undergraduate Estimation course, who was very enthusiastic about statistics and my insistence on modelling rather than mathematical validation. He may have been the only one in the class, as my students always complain about not seeing the point in slides with no mathematical result. Like earlier this week when after 90mn on introducing the bootstrap method, a student asked me what was new compared with the Glivenko-Cantelli theorem I had presented the week before… (Thanks anyway to David for his vote and his kind words!)

## Archive for ASA

## what is your favorite teacher?

Posted in Kids, Statistics, University life with tags American Statistical Association, Amstat News, ASA, bootstrap, estimation class, Glivenko-Cantelli Theorem, mathematics and statistics, teaching, Université Paris Dauphine on October 14, 2017 by xi'an## errors, blunders, and lies [book review]

Posted in Books, Kids, Statistics, University life with tags and lies, ASA, blunders, book review, CHANCE, CRC Press, errors, introductory textbooks, Pierre Simon Laplace, The Lady Tasting Tea on July 9, 2017 by xi'an **T**his new book by David Salsburg is the first one in the ASA-CRC Series on Statistical Reasoning in Science and Society. Which explains why I heard about it both from CRC Press [as a suggested material for a review in CHANCE] and from the ASA [as mass emailing]. The name of the author did not ring a bell until I saw the line about his earlier The Lady Tasting Tea book, a best-seller in the category of “soft [meaning math- and formula-free] introduction to Statistics through picturesque characters”. Which I did not read either [but Bob Carpenter did].

The current book is of the same flavour, albeit with some maths formulas [each preceded by a lengthy apology for using maths and symbols]. The topic is the one advertised in the title, covering statistical errors and the way to take advantage of them, model mis-specification and robustness, and the detection of biases and data massaging. I read the short book in one quick go, waiting for the results of the French Legislative elections, and found no particular appeal in the litany of examples, historical entries, pitfalls, and models I feel I have already read so many times in the story-telling approach to statistics. (Naked Statistics comes to mind.)

It is not that there anything terrible with the book, which is partly based on the author’s own experience in a pharmaceutical company, but it does not seem to bring out any novelty for engaging into the study of statistics or for handling data in a more rational fashion. And I do not see which portion of the readership is targeted by the book, which is too allusive for academics and too academic for a general audience, who is not necessarily fascinated by the finer details of the history (and stories) of the field. As in The Lady Tasting Tea, the chapters constitute a collection of vignettes, rather than a coherent discourse leading to a theory or defending an overall argument. Some chapters are rather poor, like the initial chapter explaining the distinction between lies, blunders, and errors through the story of the measure of the distance from Earth to Sun by observing the transit of Venus, not that the story is uninteresting, far from it!, but I find it lacking in connecting with statistics [e.g., the meaning of a “correct” observation is never explained]. Or the chapter on the Princeton robustness study, where little is explained about the nature of the wrong distributions, which end up as specific contaminations impacting mostly the variance. And some examples are hardly convincing, like those on text analysis (Chapters 13, 14, 15), where there is little backup for using Benford’s law on such short datasets. Big data is understood only under the focus of large p, small n, which is small data in my opinion! (Not to mention a minor crime de *lèse-majesté* in calling Pierre-Simon Laplace Simon-Pierre Laplace! I would also have left the *Marquis de* aside as this title came to him during the Bourbon Restauration, despite him having served Napoléon for his entire reign.) And, as mentioned above, the book contains apologetic mathematics, which never cease to annoy me since apologies are not needed. While the maths formulas are needed.

## not an ASA’s statement on p-values

Posted in Books, Kids, Statistics, University life with tags ASA, p-values, statistical significance, testing of hypotheses, Vladimir Vovk on March 18, 2016 by xi'an

**T**his may be a coincidence, but a few days after the ASA statement got published, Yuri Gurevich and Vladimir Vovk arXived a note on the Fundamentals of p-values. Which actually does not contribute to the debate. The paper is written in a Q&A manner. And defines a sort of peculiar logic related with [some] p-values. A second and more general paper is in the making, which may shed more light on the potential appeal of this formalism…