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

Rating:
Summary: Pleasing intro to statisctics for lay(wo)men
Review: An intriguing story based introduction to the fast field of
statistics. No formulas but still plenty of math terms explained
as easily as possible. The life stories of many statisticians
are combinded with the history of certain statistical problems.
This book showed me how huge the field of stastics is.
Statistics and Probability seem now to be scientific issues
on not just ways for politicians to cheat the public. In
everyday life, any mention of a statistic result causes at best
a compasionate smile. But this book changed that for me and I'd
like to learn more about this topic.

Rating:
Summary: Humane biographic sketches of numeric people
Review: David Salsburg has written gracefully engaging and humane sketches regarding some people who made diversely valuable contributions to methods for analyzing data.

I knew a number of people profiled in this book. A prior tenant of the house in which I grew up was biometrician Chester Bliss; from Salsburg, I learned that Bliss lost his job during the Depression, lived with R. A. Fisher for a few months in England before finding a job at the Leningrad Plant Institute. He barely escaped Russia in advance of a bloody Stalinist purge. I never would have suspected that this good-natured, elderly statistican had had such an eventful life.

Another chapter concerns Princeton Professor Samuel Wilks. Unbeknowst to Salsburg, since this has been otherwise unreported, at the time of his death in 1964, Wilks headed the Science Advisory Board for the U.S. National Security Agency. (I learned this from looking in the archive of Wilks' professional papers.) A small number of people within the Princeton mathematics department contributed quietly to U.S.-British efforts to read German codes during World War II and continued this activity during the Cold War. Wilks recruited my father to Princeton in the early 1950s. I was glad to read the chapter on Sam, who died when I was eight.

I also enjoyed the chapter on my uncle, John W. Tukey. Likewise, I enjoyed a profile of the English cryptologist and Bayesian statistician I. J. "Jack" Good. One of Tukey's more noted contributions to the advancement of the information age was the Fast Fourier Transform algorithim, which enabled digital computers to solve certain problems that formerly required analog computers. Tukey's 1965 FFT paper, with IBM programmer J. Cooley, draws from a paper by Good.

I enjoyed the mention of diverging opinions among statisticians during the 1950s regarding the potential health impacts of smoking. Fisher and Tukey's friend Mayo Institute biometrician Joseph Berkson were among those skeptical of studies which argued that smoking increased incidence of cancer. Salsburg suggests that a 1959 paper by another friend of Tukey's, Jerome Cornfield, was influential in shaping this debate.

Salsburg is a fine, highly readable writer. I believe that for years he was involved in a program at the University of Connecticut to record interviews with statisticians for the history of science. From these and other sources, he has painted pictures, going behind the numbers and mathematics in professional writings to capture something of the flavor of the authors as people. In so doing, he has done their memories kind service and helped explain, in accessible, non-technical ways, why interpreting data is important, in diverse ways.







Rating:
Summary: A laidback "Men of Mathematics" for statisticians
Review: 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

Rating:
Summary: I don't like math but this book kept me reading
Review: I thought this was a great book. The author uses interesting examples for the use of statistics in every day life and we discover how this complicated science has many everyday applications

Rating:
Summary: Noble effort, and entertaining.
Review: It should come as no surprise to any reader that a 300 page collection of anecdotes might fall a bit short in realizing the implied goal in Salsburg's subtitle. He attempts to explain the paradigmatic shift in science from a Newtonian determinism to a probabilistic worldview by focusing on the statisticians themselves. The reader is often left with a desire for more - either more explanation of the paradigm shift or more anecdotes.

Nonetheless, I found this volume entertaining. I was fascinated by the newness in this field. Certainly nothing in my education led me to believe that virtually every aspect of social science research and statistical analysis is a 20th century invention. Who would have thought that the essence of 21st century social science research would be so well-anchored in agricultural studies and, perhaps most importantly, in the quality control efforts by master brewers at Guinness?

Salsburg intends to write to a non-statistical audience in language that can be understood without mathematic symbols. In this he is only partly successful. He does avoid technical symbols and most technical jargon, but in doing so he is often too vague to make his point clear. Even with three years of graduate statistics (from a social science perspective), I often found myself unsure of his explanations.

In the final analysis, Salsburg's description of the "statistical revolution" in science is really more of a sketch than a portrait. The significances of a shift from certainty to probability cannot be easily explained, but I will give him credit for trying to do so. That he is able to deal with this shift without explicitly commenting on the implications of this shift for religion, values, meaning, and justice is perhaps one of this book's major strengths.

Unfortunately, Salsburg concludes with a critique of the statistical revolution that may weaken the impact of his stories. Those desperately holding onto a Newtonian worldview could use this critique to discount 20th century science, especially social science. If, as Salsburg suggests, we are on the cusp of another paradigm shift, any post-statistical revolution is unlikely to be advanced by those continuing to resist the statistical one.

Rating:
Summary: What happened to Frank P. Ramsey?J M Keynes?
Review: Salsburg(S) does an excellent job discussing the historical development of the field of statistics in the 20th century.He has a way of writing that blends current statistical theory with the development of statistics over time from a historical perspective with the individuals who made it all happen,such as Neyman-Pearson and Sir Ronald Fisher.In this book he is close to Ian Hacking in the manner in which he weaves his story.This reviewer has a few quibbles.First,in S's discussion of the personalist(subjectivist)theory of probability,only de Finetti and Savage are covered.Since Frank Ramsey's 1922 and 1926 contributions to the subjective theory of probability,unfortunately combined with error filled critiques of John Maynard Keynes's logical theory of probability,were published BEFORE the work of de Finetti and Savage,he definitely deserved to have a prominent place in any book dealing with the history of probability and statistics.Second,there are a number of errors made in the all to brief discussion of Keynes and his logical theory of probability in his 1921 book,A Treatise on Probability(TP).Contrary to S(p.112,p.305),Keynes never received a doctorate in philosophy for writing the TP because the TP is not a doctoral dissertation.The TP was a thesis submitted for a fellowship, successfully, in 1909 at Cambridge.Keynes added a Part V to his thesis in the period from 1910-1914 to complete his TP.S commits another error when he chacterizes Keynesian economic policy as the manipulation of monetary policy.It is the manipulation of both fiscal and monetary policy.Finally,Keynes's probabilities are primarily intervals with a lower and an upper bound,not ordinal rankings as suggested by S.S's flawed appraisel involves a failure to translate Keynes's definition of the term "nonnumerical",which means"not by a single numeral but by two numerals".Finally,S is in too much of a hurry to take the side of Neyman,a deductivist, in his debates with Fisher,an inductivist,about significance levels(p-values) and confidence intervals.Neyman's justification for confidence intervals is badly flawed.It essentially boils down to an arbitrary "act of will" on the part of the researcher.Fisher,who was well acquanted with Keynes's logical theory of probability,realized that Neyman's "reasoning" was actually an evasion.Unfortunately,Fisher never was clear about his reservations .Fisher simply needed to come right out and say that a 95% confidence interval means that the researcher is 95% confident that the particular parameter,say the mean,lies in that interval.Of course,this conclusion follows from the proportional syllogism,which is part of the logical theory of probability.Neyman,who was a frequentist,ends up in a quagmire of his own creation because he did not want to allow any "inductive" concepts into his theory.

Rating:
Summary: Wonderfully well written, entertaining, and informative
Review: The intense media attention given to the proof of Fermat's Last Theorem a few years ago was followed by the publication of many books on mathematics for non-mathematicians. Dr. Salsburg's book is arguably among the best of them. It has many interesting and illuminating anecdotes about the most influential statisticians in the early 20th century, which is when the Statistical Revolution (as aptly called by the author) took place. Important developments are clearly explained in their historical context, and their implications for current (i.e., 21st century) scientific research are given. The student of Statistics will get to know the people behind the names mentioned in the textbooks. The book is non-technical and written for the general public, but as a statistician myself I can say that I was no less than delighted reading it. In fact, two chapters (on probit and sample selection) deal with concepts I'm using in an epidemiological manuscript!

Rating:
Summary: great look at statistics in the 20th Century
Review: 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.

Rating:
Summary: strictly for non-Bayesians
Review: This book has many good qualities. It is easy to read, and I enjoyed reading it. It is also cheap and light in weight, with short chapters, so I read most of it traveling on the subway. The historical anecdotes about famous statisticians are interesting and enliven the book. But it has two drawbacks.

First, its references are not really up to modern standards. If something catches your eye and you want to follow it up, the book does not make it easy for you. There are several pages of references, but they are not linked to the text and they are not arranged by topic.

The second drawback will probably pass most readers by, but is more serious. David Salsburg appears to be a resolute non-Bayesian. He mentions some Bayesian ideas (one chapter out of 29 is the "Bayesian Heresy"), but he is clearly unsympathetic. The problem about this is that he manages to miss entirely the fascinating story of how some demonstrably wrong ideas ("classical statistics") took over from Bayesian statistics in the early twentieth century and have held sway ever since. In many ways it is classic Kuhn - we are waiting for the "classical statistics" guys to die off. Like all stories about science there are many fascinating subplots, but Salsburg manages to miss it all.

He also, of course, helps to educate the lay reader (at whom the book is aimed) in some seriously wrong ideas.

Rating:
Summary: Anecdotal history of Twentieth Century statistics
Review: This book introduces the personalities behind the names found on the famous statistical works of the Twentieth Century. Beginning with Francis Galton, the founder of the journal Biometrika and the discoverer of the uniqueness of fingerprints, Salsburg outlines the major developments and developers of modern statistics. In order to make the book accessible to general readers, he strenuously avoids mathematical formulas or charts, keeping his discussion focused on the people behind the math. He relates such tales as the origin of "Student t-tests", which go back to William Sealy Gossett, a statistician employed by the Guiness Brewing Company, who was forbidden by his company to publish his work, hence his use of the pseudonym "Student". The text is organized into many short chapters, each only a few pages long. At the end of the book is a timeline, covering the publication dates of key papers in statistics and their authors, followed by an annotated bibliography of suggested works for further study and a list of materials used in the book. There is also an index that includes names of people and institutions as well as general statistical topics.

I picked up the book because I was intrigued by the sub-title: "How Statistics Revolutionized Science in the Twentieth Century." Unfortunately, the book has very little about this topic, and probably much more could be said- -it certainly would make for an interesting volume. The numerous stories about the people behind the developments in statistics are quite interesting, nevertheless. Unfortunately, Salsburg goes a bit too far in avoidance of math. He describes statistical topics in a very general fashion, so general in fact, that readers who don't know statistics are left completely in the dark. If he had only added a graph here and there to demonstrate the topics visually, interested general readers might gain a better sense of what each statistical personality accomplished. He also has a habit of laying out the details of interesting experiments in such fields as medicine or agronomy which led to the development of new statistical approaches. But then he leaves us hanging, not following up with the results of the experiments and the scientific facts that were learned through using the statistics.

Nevertheless, the book is quite engaging, and I've found it has at least sensitized me to importance of statistics (without actually teaching me how to do any statistics). It would be very valuable reading for statistics students, enabling them to get to know the people who wrote the famous papers in their field and learn about the circumstances that led to their discoveries.