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103 of 113 people found the following review helpful:
4.0 out of 5 stars
A laidback "Men of Mathematics" for statisticians, July 16, 2001
This review is from: The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century (Hardcover)
David Salsburg's book "The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century" (W.H. Freeman & Co., 340 pp., $23.95) celebrates the lives of two dozen great statisticians. Short biographies of statistical innovators -- such as Francis Galton, Karl Pearson, Edward Deming, John Tukey and the most important of all, Ronald A. Fisher -- might seem of limited interest. Yet, over the past century, statisticians probably have done more to help us understand the real world than philosophers, who are endlessly profiled in countless books. When discussing what has helped him in his work, Nobel Laureate physicist Stephen Weinberg has undiplomatically referred to "the unexpected uselessness of philosophy," while praising the "unexpected usefulness of mathematics." The fecklessness of philosophy stems in part from the anti-statistical bias of the central tradition in European philosophy. Going back to Plato, philosophers have tended to assume that reality is based on abstract essences that could be described by geometry or words. In truth, though, the natural and human worlds appear to be probabilistic affairs. Statistics have thus proven crucial for describing subjects as commonplace as differences in human intelligence, as esoteric as quantum mechanics, and as life-or-death as the testing of new medicines. This ignorance of statistics also plagues our public life. Veteran pundit James J. Kilpatrick has rightly argued that young journalists absolutely ought to study statistics in college. For instance, the press is constantly fouling up stories on topics as important as health or race because reporters don't understand that when a scientist says that "A correlates with B," he does not necessarily mean "A causes B." The other three possibilities are: 1. "B causes A." 2. "Something else causes both A and B." Or, 3. "A and B aren't actually related, they just looked that way because of random luck or a mistake in our study." The founder of modern nursing, Florence Nightingale, said, "To understand God's thoughts, we must study statistics, for these are the measure of His purpose." As the inventor of the pie chart, which she used to show that bad medical care was killing more British soldiers than enemy bullets, she makes a brief appearance in Salsburg's engaging "The Lady Tasting Tea." The whimsical title refers to a Cambridge University tea party at which a lady insisted, "Tea tasted different depending upon whether the tea was poured into the milk or whether the milk was poured into the tea." Most of the scientists attending thought this nonsense, but the great R.A. Fisher immediately devised a careful experiment that was largely capable of ruling out the effect of random luck. In Fisher's experiment, the lady correctly identified each cup. Fisher published two crucial books in 1925 and 1935 that showed scientists for the first time how to design experiments that would produce statistically valid results. To avoid scaring off readers, Salsburg left out all mathematical formulas, but that's a little like a history of art without pictures. Still, for anyone somewhat familiar with the main statistical techniques, this is a pleasant introduction to the men and women behind them. Of course, statisticians generally try not to lead lives of lurid drama. Yet, quite a few were persecuted by Hitler, Mussolini, and Stalin. For example, a brilliant agricultural statistician named Chester Bliss couldn't find a job in America during the Depression, so Fisher landed him a post at the Leningrad Plant Institute. One day, his Russian girlfriend told him that the Communist Party had decided he was an American spy. As his inquisition began, Bliss immediately went on the offensive, denouncing the communist experts for bad statistical techniques. He also called communism "the gospel according to Saint Mark and Saint Lenin." Astonished, Stalin's minions decided he was too honest to be a spy. So, the communists left him alone for months until they eventually realized that while he wasn't a spy, he was an anti-communist. He had to flee for his life. The Stalinists were even more offended by the discipline of statistics than were the Nazis and Fascists. Salsburg describes why in a passage of black comedy: "The mathematical concept of a 'random variable' lies at the heart of statistical methods. The Russian translation for 'random variable' is 'accidental magnitude.' To the central planners and theoreticians, this was an insult. All industrial and social activity in the Soviet Union was planned according to the theories of Marx and Lenin. Nothing could occur by accident. ... The applications of mathematical statistics were quickly stifled." Salsburg makes clear that the early statisticians were largely interested in developing techniques for studying the inheritance of intelligence, an inquiry that continues to attract furious denunciations even today. Francis Galton -- who invented fingerprinting, the weather map, and the silent dog whistle -- was the smarter half-cousin of Charles Darwin. Their common grandparent was the near-genius Erasmus Darwin, who had proposed his own version of a theory of evolution. Not surprisingly, Galton was fascinated by how intelligence tends to run in families. In 1869, Galton wrote the first book on the subject, "Hereditary Genius." To aid his research, he invented the correlation coefficient and the concept of "regression to the mean," which explained why smart parents tend to have less smart children. Galton invented the term "eugenics" to describe the now highly unfashionable field of studying how to improve the human genetic stock. He suggested encouraging the finest young men and women to marry. Fisher, in fact, was such an enthusiast for eugenics that during World War II he was falsely accused of being a fascist and blocked from helping with Britain's war effort. Fisher's belief in the value of eugenics led him to become perhaps the leading mathematical geneticist of his generation. Advances in the Human Genome Project, genetic engineering, and sperm and egg selection are now beginning to make it feasible for couples to choose some of their child's genes. So, the controversies over eugenics are beginning all over again. But pro or con, anyone attempting to understand the coming impact of the new genetic technologies will need to use the statistical techniques invented by Galton and Fisher. -- Steve Sailer
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40 of 41 people found the following review helpful:
5.0 out of 5 stars
a biostatisticians view of 20th century statistics, January 24, 2008
This review is from: The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century (Hardcover)
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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37 of 38 people found the following review helpful:
5.0 out of 5 stars
Excellent description of how statistics was founded, January 1, 2002
This review is from: The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century (Hardcover)
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.
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