The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century

Ultimately, the various tales herein are unified in a single theme: the conversion of science from observational natural history into rigorously defined statistical models of data collection and analysis. This process, usually only implicit in studies of scientific methods and history, is especially important now that we seem to be reaching the point of diminishing returns and are looking for new paradigms of scientific investigation. The Lady Tasting Tea will appeal to a broad audience of scientifically literate readers, reminding them of the humanity underlying the work. --Rob Lightner

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0 of 0 people found the following review helpful:

A walk down memory lane, if you already have the memories..., 2008-12-29
This book reads like your grandfather's leisurely and meandering retelling of your family history. Like your grandfather's tales, it seems to have been motivated by a specific question (here, the subtitle, "how statistics revolutionized science in the twentieth century"), but after a good initial effort to answer that question, it gets sidetracked into tidbits and trivia about the characters in the story until they become the main focus. As when you listen to your grandfather, this is quite enjoyable, provided that you already know who these people are, and you already feel that their story is relevant to you. Just as your grandfather tries to provide some historical perspective but ultimately prefers to just revisit his memories, this book provides some context to why the work these people did was important, but it is only a sketch that will leave the uninitiated scratching their heads while those with the same background will knod in agreement as they fill in the missing details from their own memories. Finally, though you leave with a well-rounded impression of some of the cast, the description of others borders on being a vague panegyric. At the end, your original question is briefly reconsidered, but the answer is unsatisfying and leaves you with the impression that grandpa really just wanted to walk down memory lane.

If you read this book, you should do so because you want to know more about the people whose names you have read throughout your education and work in statistics. If you are interested in learning about how statistics changed science, I would look elsewhere. Apart from a good basic discussion of the differences between deterministic and probabalistic models of the world in the early chapters, this is not treated in enough detail to teach you very much you do not already know, and says essentially nothing about newer approaches like complexity theory. If you are hoping to find explanations of statistical concepts in plain language, turn around and walk away. The author's efforts to expunge the mathematical details has also taken away the conceptual core (with a few exceptions). People who already know the math or the concepts will recognize what he has left behind well enough to gain their bearings, but others will be unable to appreciate why the work that these statisticians did was important, and will be left wondering why they should care about their life histories. In the end, both the expert and the novice reader are done a disservice by this approach.


0 of 0 people found the following review helpful:

Amazing, 2008-11-01
To read the book was a beautiful experience. So many crucial things of statistic history presented as short and clear stories.
In some chapters I could, at last, understand difficult concepts (martingale, fuzzy).
I did want the book never ended, and I have not english as my first language (So forgive my mistakes in Shakespeare language)


0 of 0 people found the following review helpful:

Great book on the history of statistics in the 20th Century, 2008-09-15
This is the best book I've found on the recent history of statistics. The book has a lot of detail about the rolls that Pearson, Fisher, Neymam, Bayes, Tukey and others played in the development of statistical theory and practice. The book does a good job of detailing the utility of statistical theory while pointing out the well-known flaws of null hypothesis testing.


1 of 1 people found the following review helpful:

Inspiring!, 2008-05-08
I really enjoyed this book.
It makes you understand that science is not perfect, that not everybody agrees or thinks the same about the issues, and that there is always much to be done.
It was interesting to know a little of the lives of the people behind the ideas, and also how often the desire to resolve practical matters pulls science.


27 of 27 people found the following review helpful:

a biostatisticians view of 20th century statistics, 2008-01-24
The Lady Tasting Tea is a new book by David Salsburg (a Ph.D. mathematical statistician, who recently retired from Pfizer Pharmaceuticals in Connecticut). The title of the book is taken from the famous example that R. A. Fisher used in his book "The Design of Experiments" to express the ideas and principles of statistical design to answer research questions. The subtitle "How Statistics Revolutionized Science in the Twentieth Century" really tells what the book is about. The author relates the statistical developments of the 20th Century through descriptions of the famous statisticians and the problems they studied.

The author conveys this from the perspective of a statistician with good theoretical training and much experience in academia and industry. He is a fellow of the American Statistical Association and a retired Senior Research Fellow from Pfizer has published three technical books and over 50 journal articles and has taught statistics at various universities including the Harvard School of Public Health, the University of Connecticut and the University of Pennsylvania.

This book is written in layman's terms and is intended for scientists and medical researchers as well as for statistician who are interested in the history of statistics. It just was published in early 2001. On the back-cover there are glowing words of praise from the epidemiologist Alvan Feinstein and from statisticians Barbara Bailar and Brad Efron. After reading their comments I decided to buy it and I found it difficult to put down.

Salsburg has met and interacted with many of the statisticians in the book and provides an interesting perspective and discussion of most of the important topics including those that head the agenda of the computer age and the 21st century. He discusses the life and work of many famous statisticians including Francis Galton, Karl Pearson, Egon Pearson, Jerzy Neyman, Abraham Wald, John Tukey, E. J. G. Pitman, Ed Deming, R. A. Fisher, George Box, David Cox, Gertrude Cox, Emil Gumbel, L. H. C. Tippett, Stella Cunliffe, Florence Nightingale David, William Sealy Gosset, Frank Wilcoxon, I. J. Good, Harold Hotelling, Morris Hansen, William Cochran, Persi Diaconis, Brad Efron, Paul Levy, Jerry Cornfield, Samuel Wilks, Andrei Kolmogorov, Guido Castelnuovo, Francesco Cantelli and Chester Bliss. Many other probabilists and statisticians are also mentioned including David Blackwell, Joseph Berkson, Herman Chernoff, Stephen Fienberg, William Madow, Nathan Mantel, Odd Aalen, Fred Mosteller, Jimmie Savage, Evelyn Fix, William Feller, Bruno deFinetti, Richard Savage, Erich Lehmann (first name mispelled), Corrado Gini, G. U. Yule, Manny Parzen, Walter Shewhart, Stephen Stigler, Nancy Mann, S. N. Roy, C. R. Rao, P. C. Mahalanobis, N. V. Smirnov, Jaroslav Hajek and Don Rubin among others.

The final chapter "The Idol with Feet of Clay" is philosophical in nature but deals with the important fact that in spite of the widespread and valuable use of the statistical methodology that was primarily created in the past century, the foundations of statistical inference and probability are still on shaky ground.

I think there is a lot of important information in this book that relates to pharmaceutical trials, including the important discussion of intention to treat, the role of epidemiology (especially retrospective case-control studies and observational studies), use of martingale methods in survival analysis, exploratory data analysis, p-values, Bayesian models, non-parametric methods, bootstrap, hypothesis tests and confidence intervals. This relates very much to my current work but the topics discussed touch all areas of science including, engineering in aerospace and manufacturing, agricultural studies, general medical research, astronomy, physics, chemistry, government (Department of Labor, Department of Commerce, Department of Energy etc.), educational testing, marketing and economics.

I think this is a great book for MDs, medical researchers and clinicians too! It will be a good book to read for anyone involved in scientific endeavors. As a statistician I find a great deal of value in reviewing the key ideas and philosophy of the great statisticians of the 20th Century.

I also have gained new insight from Salsburg. He has given these topics a great deal of thought and has written eloquently about them. I have learned about some people that I knew nothing about like Stella Cunliffe and Guido Castelnuovo. It is also touching for me to hear about the work of my Stanford teachers, Persi Diaconis and Brad Efron and other statisticians that I have met or found influential. These personalities and many other lesser-known statisticians have influenced the field of statistics.

The book includes a timeline that provides a list in chronological order of important events and the associated personalities in the history of statistics. It starts with the birth of Karl Pearson in 1857 and ends with the death of John Tukey in 2000.

Salsburg also provides a nice bibliography that starts with an annotated section on books and papers accessible to readers who may not have strong mathematical training. The rest of the bibliography is subdivided as follows: (1) Collected works of prominent statisticians, (2)obituaries, reminiscences, and published conversations and (3) other books and article that were mentioned in this book.

The book provides interesting reading for both statisticians and non-statisticians.

Dennis Littrell comments in his review that he missed the fact that the formulas common in mathematical statistics were missing. For statisticians and mathematicians such things help put extra meat bewteen the bread in the sandwich. But personally I do not see where that would contribute much conceptually to the book and it could have the effect of turning off the non-mathematically inclined medical researchers and other medical professionals who could learn to appreciate the role of statistics in the scientific advances in the twentieth century. Also note that I have the hardcover version of the book. The only difference between the hardcover and the paperback edition is the reduced price. Publishers often do that with popular books to increase sales.

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