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Lady Tasting Tea (Englisch) Taschenbuch – Mai 2002

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Science is inextricably linked with mathematics. Statistician David Salsburg examines the development of ever-more-powerful statistical methods for determining scientific truth in The Lady Tasting Tea, a series of historical and biographical sketches that illuminate without alienating the mathematically timid. Salsburg, who has worked in academia and industry and has met many of the major players he writes about, shares his subjects' enthusiasm for problem solving and deep thinking. His sense of excitement drives the prose, but never at the expense of the reader; if anything, the author has taken pains to eliminate esoterica and ephemera from his stories. This might frustrate a few number-head readers, but the abundant notes and references should keep them happy in the library for weeks after reading the book.

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 -- Dieser Text bezieht sich auf eine vergriffene oder nicht verfügbare Ausgabe dieses Titels.


“Highly readable and well-written. Give it to someone you want to delight.” ―Alcan R. Feinstein, M.D., Sterling Professor of Medicine and Epidemiology, Yale University School of Medicine

“A fascinating description of the kinds of people who interacted, collaborated, disagreed, and were brilliant in the development of statistics.” ―Barbara A. Bailar, National Opinion Research Center

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sehr empfehlenswert für denjenigen, der die Geschichte der Statistik im 20. Jahrhundert auf unterhaltsame Weise kennenlernen will. Geschrieben mit Witz und Herz.
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HASH(0xa1ec5f00) von 5 Sternen a biostatisticians view of 20th century statistics 24. Januar 2008
Von Michael R. Chernick - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
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.
113 von 124 Kunden fanden die folgende Rezension hilfreich
HASH(0xa1ec92f4) von 5 Sternen A laidback "Men of Mathematics" for statisticians 17. Juli 2001
Von Amazon Customer - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
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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HASH(0xa1ec3894) von 5 Sternen Good for non-statisticians 23. Juli 2001
Von Ein Kunde - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
This is a pretty good book for non-statisticians, but then why would a non-statistician ever want to read it? As an applied statistician I was repeatedly frustrated by the high-level summaries of most topics with no real depth. For example, the section that discusses bootstrapping was so cursory that without prior knowledge and experience I would not have had a clue what bootstrapping is or how it works. I know the target audience was the general public, but in some cases a little more depth would have moved this from being only a good to a great book. One thing that I found amusing was the cover art--didn't the graphic artist even read the book? If you read the story that is the basis for the book title you would NEVER have shown a cup of tea with lemon. One does not use lemon and milk in tea.
If you are looking for an excellent book that covers the role statistics has played in industry and science and has some depth, consider George Box's "Box on Quality and Discovery" ... This book is approachable for non-statisticians and will keep us statisticians awake!
41 von 44 Kunden fanden die folgende Rezension hilfreich
HASH(0xa1ec9144) von 5 Sternen Statistics humanized and triumphant 24. Juli 2002
Von Dennis Littrell - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
The title refers to the story about the English lady who believed she could tell by tasting whether the milk had been added to the tea or the tea added to the milk. We find out here that apparently she could. At least in the small sample of cases recorded, she "identified every single one of the cups correctly." (p. 8)
The question--and this is the question that statisticians are forever trying to answer--is, was the result significant? Or how much faith should we put in such a result? What is the probability that such a result comes to us by chance rather than by causation? Did she simply guess right ten times in a row? Or, more saliently, how many times would she have to guess right before you'd be a believer? Or, more rigorously, how many times out of how many trials would she have to guess right before we can be confident that she isn't just guessing?
Statistics then is a way of understanding and appreciating events without reference to causation. How cigarette smoking causes lung cancer is not exactly known. The fact that cigarette smoking does indeed cause lung cancer is demonstrated by a clear statistical correlation between smoking and the instance of lung cancer. But is a statistical correlation proof?
Salsburg's very readable book is a narrative about the mathematicians who have tried to answer this and other statistical questions. The emphasis is on the mathematicians themselves, not on their mathematics. Indeed, following a time-honored "rule" in the book publishing business, a rule that insists that you lose "x" number of readers for every mathematical formula that appears on your pages, Salsburg has elected to use a grand total of zero.
I was a little disconcerted about this. To encounter Bayes's theorem or any number of other statistical ideas and see not a single formula or mathematical expression was to me like reading a joke book without any jokes in it. But for those who have heard the jokes and are only interested in the joke tellers and their problems, this is indeed a fascinating book. It is ironic that this "non-mathematical" book is probably best appreciated by those with some experience with statistics. Such readers I suspect will be quite pleased to read about the lives of such greats in statistical theory and methods as Karl Pearson, R. A. Fisher, William Sealy "Student" Gosset, John Tukey, etc. Salsburg focuses on the problems that the individual mathematicians encountered and the solutions they came up with.
Here's an example of how Salsburg does this neat trick of talking about mathematics without using any mathematics. He asks, "What is the central limit theorem?" (p. 84) and answers thusly:
"The averages of large collections of numbers have a statistical distribution. The central limit theorem states that this distribution can be approximated by the normal probability distribution regardless of where the initial data came from. The normal probability distribution is the same as Laplace's error function. It is sometimes called the Gaussian distribution. It has been described loosely in popular works as the bell-shaped curve."
Perhaps this does work for a lot of people, but I think this book would be improved if there were an appendix with a list of ideas, presented in mathematical form. For a new edition, Salsburg might want to do something like that. Then this interesting book would also be a work of reference.
My favorite method learned here is on page 236. Salsburg describes how John Tukey believes one should tally. Instead of making vertical lines and crossing every fifth one (which is what I have done for decades) Tukey recommends "a ten-mark tally. You first mark four dots to make the corners of a box. You then connect the dots with four lines, completing the box. Finally, you make two diagonal marks forming a cross within the box."
That statistical ideas are inexorably tied up with the ideas of probability is explored in the final chapter of the book, "The Idol with Feet of Clay." Salsburg observes, along with Thomas Kuhn, that we are forever describing reality with "a model...that appears to fit the data," but as the data accumulates our model "begins to require modifications." (p. 293) Reality in this sense is the postulated "universe" of the statistician, and our experiences and "laws" the result of "samplings" of that universe. Salsburg, citing L. Jonathan Cohen, goes on to recall Seymour Kyberg's "lottery paradox" which makes it clear that statistical/probabilistic "proofs" run into logical problems. He then asks if we really understand probability. He recalls the notion of "personal probability" (something I used to call "psychological probability") in which we appreciate the probability of something happening in terms of what effect it might have on us personally. Thus a small chance of getting something exceeding important to us (such as winning the lottery) might be worth paying more for the ticket than it is objectively worth. Salsburg concludes that we really do not understand probability except in the grossest sense (e.g., "50/50" or "almost certain"). Then he asks, does it matter? His answer suggests quantum mechanics in which we work with probabilities without any pretense of grasping underlying "laws."
Salsburg ends the book with a yearning for a new paradigm without feet of clay. I suspect he has in mind the undeniable and always troubling fact that the best that can ever be said about a sampling is that it has a certain probability of being an accurate reflection of the entire universe. However, my guess is that we will continue to have to be satisfied with "only" probabilistic knowledge; indeed that knowledge itself will always be subject to some degree of doubt. I might even conjecture that all real world knowledge, yearn as we might for certainty, is probabilistic.
12 von 13 Kunden fanden die folgende Rezension hilfreich
HASH(0xa1ec8684) von 5 Sternen Stimulating, delightful, informative, mildly frustrating 19. Mai 2001
Von Bear C. Braumoeller - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
This is a delightful glimpse into the history of statistics that gives users and non-users alike a clear view of the perspectives and personalities that shaped the discipline. It will be enjoyable for most non-specialists and a must-read for anyone who has taken classes in statistics. The latter group will undoubtedly find the book somewhat frustrating, in that a few equations here and there would add significantly to Salsburg's descriptions of e.g. the elegance of certain distributions or the spare beauty of others. Avoiding math was undoubtedly the right decision from a mass-market point of view, but (enterprising authors, take note!) a companion volume with all of the equations would make this one immensely appealing for introductory statistics classes.
My only real complaint is that the narrative style is at times rather abrupt. It could hardly be otherwise, given the task that Salsburg sets for himself. Imagine, for example, a book on the history of cooking that described the evolution of new dishes without including any recipes: the author would be limited in how much he can describe, and when he moved on inquisitive readers would be left thinking, "Well, wait, now -- how much basil? How much garlic?" etc. Similarly, the limits on the amount of detail in which the author can describe advances in statistics often left me feeling as though we'd moved on when only part of the story had been told. I can't imagine how it could be otherwise, though, and to give credit where credit is due the author has a genuine knack for boiling complicated statistical issues (e.g. controversies surrounding the Neyman-Pearson approach to hypothesis testing) down into very straightforward language.
The final chapter is stimulating in a big-think sort of way: Salsburg, having documented the development of statistics over the past century, reviews its sometimes questionable foundations and suggests that before too long it may be replaced with something else. What that "something else" might be is tantalizingly vague, but the chapter serves as a useful reminder that revolutionary modes of thinking, like those that started the statistical revolution, are always waiting in the wings.
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