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

Product Description

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.


Excellent description of how statistics was founded   January 1, 2002
Charles Ashbacher (Marion, Iowa United States(cashbacher@yahoo.com))
22 out of 22 found this review helpful

I have taken courses in statistics, taught it many times and solved several statistical problems that have appeared in journals. But until I read this book, I never really thought about it in so deep and philosophical a manner. Which is most unusual, in that it is a book written to a popular audience. Some of the very deep and critical problems raised consider questions such as, "How do you deal with outliers?" An outlier is a data point that differs from the others by a great deal. The fact that it is a data point means that it is part of the sample, but the large differences from the others means that there are valid reasons to consider it flawed. Given these differences, including or excluding an outlier can lead to substantial changes in the results.
Other issues concern the accuracy of measurement, for example, when can specific tests be applied and what consequences can be associated with the results. We saw an example of such complexity in the 2000 presidential election in the United States. The vote was essentially a tie, with the differences being well within all possible measures of sampling error. As some of the wiser news commentators pointed out, it is impossible to count every vote, an election is only an approximation of the true, unknown value. No statistician could have said it better.
Given the context, Plato's idea of the abstract form appears in this history of the development of statistics as a discipline separate from mathematics. A statistical sample is only an estimate of a value that will never be known. The key is to get an approximation that is close enough to be usable in whatever the current context is. In this respect, statistics is like engineering, where the interest is in getting usable, rather than precise information.
The author also describes many details of the historical environments that the principal early statisticians worked in. Repressive governments such as...Germany, ...Italy and the communist Soviet Union provided the backdrop of the actions of many of the people who founded statistics. While the sentiments of the author are clear, he does a good job in avoiding overt political statements.
What I liked best about the book was the clear description of the life and career of Ronald Aylmer Fisher, a man whose genius is rarely spoken of in histories of science. And yet, some of the ideas that he expounded are the basis for many of the decisions that are made in our modern society. All new medications must pass rigorous statistical tests for efficacy and safety, and virtually every scientist must subject their data to some form of statistical analysis.
This is the most interesting book on statistics that I have ever read. It caused me to think about the underlying philosophy of statistics in ways that I had never done so before. Furthermore, it is written at a level where non-mathematicians/statisticians can understand it. I soundly recommend it for personal enjoyment as well as for any course in the history/philosophy of science or statistics.

Published in Journal of Recreational Mathematics, reprinted with permission.


A gem   September 19, 2006
Yoshiro Aoki (usa)
4 out of 4 found this review helpful

I personally have a deep admiration for statistical science. Probability is everywhere, from Heisenberg to quantum mechanics to common primary school science experiments. What constitutes a good experiment? What questions should we ask? How should we interpret the data? Indeed, what data should we be expecting? What if the data are contrary to our expectations? More directly, how did these methodologies come to be? What were their motivations? Statistics and probability presently provides some of the best tools science has to offer for exploring our world, and making sense of it. These are tools forged by individuals over the past centuries with real problems to solve, despite their own very human problems. This extremely readable book helps tell their fascinating stories and the history of the evolution of statistical methods now so prevalent in our sciences. I bought this book as a gift for a doctor friend of mine, and promptly borrowed it from her after thumbing through it. I couldn't put it down for 2 days, nor stop talking about it. Absolutely a must read for anyone with a realization of the importance statistics plays in modern society. 5 easy stars for this one.


Best book for the general reader on statistics   February 18, 2002
Jeff Sutherland (Somerville, MA USA)
8 out of 9 found this review helpful

Most people don't realize that the very notion of proof, at least in the field of medicine, did not exist until 1934, when the founder of modern statistics, R.A. Fisher, invented it. He would undoubtedly have some scathing remarks on what currently passes for proof for new medical treatments. You'll read about all the great statisticians of the 20th century, many of whom fled the Nazi's, or the Russians, and wound up in the United States. One accomplished American statistician was laid off by Department of Agriculture bureaucrats in the great Depression and could only find a job in the Soviet Union under Stalin. What a great story! I met some of these guys at Stanford when I was getting my Masters Degree in Statistics in the 1970s. While they sometimes can be boring on the surface, underneath lurks a passion for reality rarely found in more superficially interesting folk. I used the text of Gumbel on how to compute the probability of a 100 year flood as the basis of my Ph.D. thesis on carcinogenesis at the University of Colorado School of Medicine Department of Biometrics in the 1980s. As a well rounded technologist, Gumbel also published a book on Four Years of Political Murder in 1922, followed by Causes of Political Murder in 1928, as a critique of the Nazis. When the Nazis came to power in 1933, he barely escaped Germany and had to hide out in Southern France. This is the best book of this type that I've read since Fermat's Enigma and it is best savored chapter by chapter over a cup of cappuccino in a Peets or Starbucks. A book for the general reader that every statistician should read!


Recommended for leisure browsing as well as study   May 18, 2001
Midwest Book Review (Oregon, WI USA)
1 out of 3 found this review helpful

Lady Tasting Tea is an unusual guide which explains how the statistical revolution in science came about, examining how statistical modeling examples were developed and used. Unique to Lady Tasting Tea is an exploration which includes no math formulas and assumes no prior grounding in math concepts, statistics or math history; making it quite accessible to lay readers, and recommended for leisure browsing as well as study.