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)

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.

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.

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.

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.

I have given several copies of this book away to my statistician colleagues, as it is an outstanding overview of the development of statistics in the twentieth century.

It is not particularly technical but it probably would appeal only to statisticians, students of statistics, and others interested in the impact of statistics on the advancement of science.

Salsburg writes a selective account of the history of statistics in the 20th century. In so doing, he tackles the philosophical issue of a scientific revolution from deterministic to stochastic thinking (he writes that this is a revolution in the Kuhnian sense). I haven't personally found another book which displays the big picture of what happened so clearly, and from that standpoint consider this book a must read on the topic. It is well written and appears to me to successfully communicate to a broad audience.

This is a very intriguing read about the history and the developments in the study of statistics throughout the twentieth century.

This is a very intriguing read about the history and the developments in the study of statistics throughout the twentieth century.

"The Lady Tasting Tea" is a valuable history of the evolution of statistical thinking. It presents a very good explanation of the meaning of "significance," "p-value" and other statistical concepts that are only dealt with as if gospel in statistics courses. It puts these concepts in context.

The book is an easy read. I found it particularly interesting because I have had the good fortune to have met, and/or worked on various committees with, several of those mentioned in the book. This includes the author. David Salsburg also provides the answer to a question that I thought for the last 35 years didn't have an answer. That is, can the lady really tell whether the milk or the tea was put in the cup first? This is the question posed in the second chapter of R. A. Fisher's classic book "The Design of Experiments."

I don't usually recommend books. However, this one I consider a "must read" for anyone who wishes to truly understand the application of statistics. It also gives us another reason to support the Guinness Brewing Company.

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.

I liked this book. It is well written, covers a lot of ground, and is a fast read for those who don't have much time. Being a scientist, though not a mathematician, I would have appreciated having the concepts explained in a little more depth, and wished there was an appendix, or a companion book, that went into the math.

I liked this book. It is well written, covers a lot of ground, and is a fast read for those who don't have much time. Being a scientist, though not a mathematician, I would have appreciated having the concepts explained in a little more depth, and wished there was an appendix, or a companion book, that went into the math.

This is not a statistics Text book, but a history of how the "science" of Statistics came about, it focuses mostly on the players, not so much the music, and as such it works extremely well. In my mind the most important topic covered is the discussion of the possible evolution of statistics. It is a sad fact that most people focus on what they know and the strengths of that knowledge, not what they don't know and the weakness that this creates. This is true not only of the lay person but the professional also. It think that is essential to understand the history of the how and why we got where we are if we are to fully appreciate what we are doing, This volume, although no exhaustive, does a high highly commendable job in this respect. Recommended for lay person and statistician alike, but particularly to those statisticians who can't shut their egos down long enough to realize that all we know are simply propositions that have not yet been falsified.

This is not a statistics Text book, but a history of how the "science" of Statistics came about, it focuses mostly on the players, not so much the music, and as such it works extremely well. In my mind the most important topic covered is the discussion of the possible evolution of statistics. It is a sad fact that most people focus on what they know and the strengths of that knowledge, not what they don't know and the weakness that this creates. This is true not only of the lay person but the professional also. It think that is essential to understand the history of the how and why we got where we are if we are to fully appreciate what we are doing, This volume, although no exhaustive, does a high highly commendable job in this respect. Recommended for lay person and statistician alike, but particularly to those statisticians who can't shut their egos down long enough to realize that all we know are simply propositions that have not yet been falsified.

This is an amazing story of what started out as a branch of mathematics, and made a space for itself as a crucial tool in science. It spans a conundrum of ideas, and bits of the people who were bold and brave enough to have these thoughts and carry them into the world to be so easily used by all and sundry. It starts with the ideas of data being the distribution from KPearson, moves into the distribution being the data from Fisher, and thence into the branches that are the synergies born out of the marriage of these two grand ideas in statistics, with new age tools like computation and applications like epidemiology.

As with all tales involving people, the book is rich with drama: the inevitable mess of personality clashes; men and women; fathers and sons; spanning countries, both rich and poor; a little epic all in itself. It's easy to get lost in a particular life, or an event; to form loyalties to one idea and constantly search for reasons to defend one's position. But the author doesn't let the reader lose the thread of the real story: it's about how statistics revolutionised science in the 20th century.

What warmed the heart the most, was to read of how these creators and analysers of cold, factual numbers, _worried_ about the impact of their findings and their science. It is an attitude that today's researchers and scientists need to keep re-orienting themselves towards. I abhor the saying: "there are three kinds of lies: lies, damned lies and statistics" because I think that that is a cop-out by people who are given the outcome of some very carefully constructed methods, and who ignore the instructions on the box that the methods come with. There is an equivalent of "caveat emptor" that comes with statistics - the last chapter in the book is a rather gentle attempt at pointing out the warning on the label.

I do thank you, Mr. Salsburg, for having written this book. All my life, I've learnt these tools, learnt to revere them without quite appreciating the vast and - may I use the word? - logical base that it was born from. This book has made me want to go back to the roots; it has made me think about where the next revolution can come from. It has made me want to help take it to the next step because I do think that the more the people know the base of uncertainty that all of our knowledge is coming from, the better a world we can be.

If I hadn't stopped to sleep, I would have made it through this easy reading, engaging book in one sitting. Dr. Salsburg takes us through the modern history of statistics one person at a time, highlighting the contributions made, while weaving the threads together to form a mosaic of modern statistics. The typical format of a chapter is to tell a statistician's story with biography, personal remembrances, and accessible descriptions of discovery. The result is a delightful, contextual story of modern statistics.

I have been the victim of statistics classes coldly stripped of any vibrancy or relevancy. This book should be required reading as an antidote against such woes. It brings vibrancy with its biographical sketches. The relevancy comes by showing us that statistics is the thing that we understand as truth. What does it mean to understand a statistic as the truth? Do statisticians agree on the foundational assumptions in the field? What major theoretical challenges face statistics today? Dr. Salsburg explores these issues with clarity. He also explores common misuses of statistics including such notable cases as:

1) how language has lead to misuse of statistical models,
2) how models have gained authority and are used by default instead of being used because they fit the question being asked, and
3) how different schools of thought within statistics has lead to ambiguity in what we know to be true.

Statistics are perhaps the most important type of math I use on a regular basis, and I thank Dr. Salsburg for providing this roadmap that provides a concise understanding of where we are and where we might want to explore.

I purchased this book for my husband, as he had talked at length with a fellow airline passenger about it. This person was reading "Tea" for a doctoral program in education, but was actually enjoying it so much she was reading it more for pleasure than for study. He has thoroughly enjoyed it too!

This is probably the best book of its kind that I have seen. It presents most of the well known, and several less well known, names in 20th century statistics and explores who they were and how they made the contributions to statistics that they did. There is little mathematical rigor in the book, so non-statisticians can follow the importance of discoveries without difficulty.

I have to read this book for a statistics course I'm taking next fall. I figured I would start early on it and at least read the first two chapters of what I assumed to be a fairly boring "history of math" book. Shows what I know about the history of math. It was not only informative but very entertaining. I read the whole book in two days, and came away from it with a better understanding of not only the history of statistics, but also a better understanding of why statistics is used, its weaknesses, its strengths, and its applications. It is a good compliment to any introductory statistics course, and I'm very glad I got the chance to read it.

It is really enjoyable to read about some of the personalities behind the development of modern statistics, but the complete absence of equations is really a problem. It feels like only half the story is there.

David Salsburg's goal is to tell a story of how statistics changed the philosophy and practice of science in the 20th century. He starts with an anecdote in the late 1920s about designing an experiment to find out if a person can tell the difference between a hot drink made by pouring milk into tea or pouring tea into milk (hence the book's title). The first eight chapters describe the introduction of statistics and the concept of designing experiments in Great Britain from the start of the 20th century up to the 1930s by Karl Pearson and R. A. Fisher. Chapters 9 to 19 tells of the spread of the use of statistics around the world and the efforts to formalize statistics and probability. Chapters 20 to 28 covers the modern application of statistics in science and industry. The last chapter discusses the meaning and utility of statistics and probability. The book includes a timeline of events and people, and an annotated bibliography.

The earlier chapters on Pearson and Fisher are more coherent than the middle ones which dip into the work and lives of different mathematicians, and the final chapters can be read individually. Salsburg should have had narrowed his scope rather than try to cover so many topics and people in such a short book. There are some annoying repeated remarks and relatively unimportant characters, for example, Henri Lebesgue's slight of Jerzy Neyman is mentioned thrice and Churchill Eisenhart appears twice for no other reason than as the person who did not meet Karl Pearson.

The book is an easy and light read about mathematicians influential in the development and application of modern statistics in the 20th century. Readers trained in statistics and with an interest in the history of its development would enjoy it most.

Kam-Hung Soh

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.

This is a really nice history of statistics over the 20th Century and the impact of statistics on advances in science and medicine. I particularly enjoyed many of the interesting anecdotes of the people involved in the development of statistics. My only complaint is that this book is very good in describing the input of East Coast statisticians but is rather lacking with respect to West Coast statisticians. West Coast statisticians are either not mentioned, or their affiliations are incorrectly stated. I was somewhat surprised that others had not noted this glaring gap, which is why I wrote this particular review and give the book only 4 stars. Otherwise, this is an interesting, entertaining read.

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 few 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 such an eventful life.

Another chapter concerns Princeton Professor Samuel Wilks. Unbeknowst to Salsburg, since 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 a chapter on my uncle, John W. Tukey. Likewise, a profile of English cryptologist and Bayesian statistician Jack Good. One of Tukey's contributions to 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.

Statisticians had diverging opinions during the 1950s regarding the health impacts of smoking. Fisher and Tukey's friend Mayo Institute biometrician Joseph Berkson were among those skeptical of studies that argued smoking increased incidence of cancer. 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 people. In so doing, he has done their memories kind service and helped explain, in accessible, non-technical ways, why interpreting data is important for society.

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.

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.

This book is in no way technical or mathematical. The author focused on explaining basic concepts and their importance without getting into the details of the stats behind it. Hence, it is a book focused on a general audience wanting to learn about the history and the characters that pushed statistics forward mainly in the 20th century (there was not much before). It should be a very easy and interesting read for someone knowledgeable of very basic math and stats (if you know what a standard deviation is, you should be ok).

The author, as a lifelong statistician, is clearly in awe of the characters described, such as Pearson and Fisher, which do seem to a bit influence his writing. For example, he is descriptive of personal meetings with some of the main described characters, which leaves me thinking that, aside from a history of stats, this may also be his personal history. Overall, this is seldom seem, and it does not much hurt the content, which is clearly very well researched and written. If you are looking to add to your knowledge and are done with pop science, this may be an interesting next book.

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

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 characterizes 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.

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.

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.

This book is almost wonderful. It presents an account of the history of statistics that I almost couldn't put down. While other reviewers have complained about the lack of detail in the descriptions of statistical principles and techniques, what this book does have, which I haven't seen anywhere else is an informal account that guides the reader to the original literature.

Salsburg is a wonderful writer. The book focuses on biographical anecdotes, some of them from personal interactions Salsburg had with his subjects, which follow the development of 20th century statistics. Salsburg's love of his subject is contageous and I repeatedly found myself staying up later than was good for me to read another chapter. I would guess that much of the book might be hard to follow for someone not familiar with statistics, but if you know what an F test or a t test is, this will give you a new appreciation of how they were invented and may well inspire you to drop by the library and read the original papers.

The weakness of this book is Salsburg's tendency to pontificate about areas of which he clearly knows next to nothing. For some bizarre reason, he dismisses chaos theory (something that certainly has been oversold to the public) saying that this discipline has no more rigorous methods than asserting the subjective similarity of Poincare maps and that there is no experimental evidence that Nature is chaotic. Apparently, Salsburg could not be bothered to glance through even the most basic literature of the field, such as the experimental confirmation of universality in period doubling, the rigorous quantitative application of the Lorenz equations (which he dismisses) to experimental data from bistable lasers, or techniques such as Grassberger and Procaccia's of rigorously determining the properties of Poincare maps. This stuff has been in the mainstream scientific literature for over 20 years, so Salsburg has no excuse to make pronouncements without learning anything about it.

In fact, Salsburg seems to use chaos as a stalking horse for an attack on determinism in physical science, but he misses the notion that Lyopunov expansion and much of KAM theory can be expressed just fine in the statistical notation that Salsburg is fond of. The burgeoning field of quantum chaology has matured enough in the for the last 10 years or so it has been possible to investigate the chaotic behavior of statistical systems rigorously and meaningfully, but Salsburg only advances his nondeterministic view of the physical world in back-handed asides, and never presents it to us straightforwardly for inspection.

Still, if you forgive Salsburg his ignorance of science, he clearly is a master of the mathematical statistics he presents and this is an engaging book that I am very glad to have in my library and which I am heartily recommending to my friends.

This is another of those books about mathematics that avoids displaying any mathematical equations or formulas, like Stephen Hawking's "A Brief History of Time." Hawking explained that his publishers told him that every line of mathematics would cut the sales by 50%. Unfortunately, the lack of mathematics makes it much harder to transmit the ideas. In many places in this book, I miss the math sorely. This book is actually harder to understand as it is, then if it gave us the relevant equations and formulas. Maybe Salsburg (or some other generous soul) could put together a web site that provides the math, referenced to chapters in the book.

Nevertheless, the book tells its stories well, and rewards (and challenges) the reader with some ideas and concepts I'd like to at least remember, if not explore further.

Chapter seven briefly outlines "the Fisherian versus the Pearsonian view of statistics," a philosophical theme that is fundamental to the application of statistics to reality. "Karl Pearson viewed statistical distributions as describing the actual collections of data he would analyze. According to Fisher, the true distribution is an abstract mathematical formula, and the data collected can be used only to estimate the parameters of the true distribution.... Pearson viewed the distribution of measurements as a real thing.... To Fisher, the measurements were a random selection from the set of all possible measurements." I'm not sure what this means, exactly.

At the end of chapter six, there is a review of a math text (Gumbel's "Statistics of Extremes," an out of print collectible being offered through amazon...) from the 1950's that makes me want to find the book and read it. The appreciation for this text is put into context by a quick sketch of the varying strengths and weaknesses of the "definitive" books in the entire realm of mathematics.

Chapter ten begins with the surprising statement, "Chaos theory is actually an attempt to undo the statistical revolution by reviving determinism at a more sophisticated level." Salsburg actually brings in the subject of chaos theory only as an introduction to the statistical concept of "goodness of fit."

World War II stories abound in this book, as it had significant impact on the lives of most of the people who brought statistics forward in the twentieth century. Nazism and fascism made martyrs of some statisticians, interrupted the careers of some, accelerated the careers of others, and drove quite a few to the United States.

Chapter eighteen provides an overview of how the link between cigarette smoking and lung cancer came to be accepted, and how the question itself drove the discipline of statistics. It also highlights a fundamental question first exposed by Bertrand Russell regarding cause and effect.

In chapter twenty-four, we learn how Japanese industry became known for superior products (notably automobiles) by heeding the advice of W. Edwards Deming, an American statistician who was unable to attract the right kind of attention in the U.S.

The final chapter steps back to get the big picture of philosophical questions that may signal the limitations of statistics as we know it, limitations that may someday be the seed of a future intellectual revolution that will bring forth an entirely new science that transcends statistics altogether. (1) Can statistical models be used to make decisions? (2) What is the meaning of probability when applied to real life? (3) Do people really understand probability? It may not be immediately apparent that there's anything worth serious consideration in these questions, but Salsburg brings them to relevant life in just a few pagess.

Brief, insightful observations on the nature of mathematical activity and mathematical community are scattered throughout the book, as if they were salt seasoning a dish to make it tastier. He notes that mathematical research rarely bears fruit without the cross pollination of discussion among mathematicians. These discussions help to expose both errors and new ideas. He has fun with the semi-facetious "law of misnomy," which claims that mathematical ideas and techniques are generally not named for the person who actually created them, but for someone else.

I enjoyed this book and learned a bit from it, but the absence of mathematics is a major shortcoming. Who would want to read a history of statistics if they weren't mathematically inclined enough to benefit from a few lines of greek letters and subscripts?

Writing a story of mathematics for a general audience is never easy (in addition to this book I am also thinking of Berlinski's "Tour of the Calculus").

Salsburg aims to provide a broad, non-technical overview of the development of modern statistical theory. The story moves from the early 20th century, the days of Pearson and "Student's t" to Deming and modern computer-aided analysis. By his own admission there are gaps in the narrative -- he focuses on those areas where he, as an academic, already has some familiarity.

He does touch on the core elements of basic statistics -- p-values, t-test, hypothesis testing -- but his work, despite the complete absence of mathematical notation, still requires more than a casual knowledge of the topic. For example, he takes for granted that the reader knows what a "normal distribution" is.

That being said, a person with at least a basic knowledge of statistics (maybe even one semester in college) is the most likely audience for this work, and would likely understand some of the basic concepts Salsburg neglects to explain.

This book is loaded with technical terms that are barely explained. True, the stories of the people involved in developing these ideas are presented in a vivid and clear style. But this does little to aid the reader in grasping these mathematical concepts so that they have meaning. After all, this book asserts that statistics has revolutionized science, and I did not feel anything like this at the end of this book. Moreover, the author glibly rejects all of chaos theory in a little bit more than 3 pages[93-6], at another point calls the law of cause and effect " a vague notion that will not withstand the batterings of pure reason"[ p 186] But what "pure reason" happens to be, the author never bothers to tell us. Moreover, he earlier asserts that "the statistical model that defines the quest of science...is also based on a statement of faith." This comment then merits a discussion of what axioms are being put forth. But again there is a conspicuous vacuum of thought.At the conclusion, Salsburg casts doubt on the entire subject of his book by stating :"I do not believe that the human mind is capable of organizing a structure of ideas that can come close to describing what is really out there." This self-contradiction coming from the mouth of a supposed science educator is a perfect reflection of the philosophical mess that constitutes this book. Not recommended.

This book is a wonderful depiction of the history of Statistics and its great contributors. Dr. Salsburg conveys the stories of the great minds of the statistical world in an insightful and interesting way.

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.

Salsburg sets himself the rather difficult task of explaining how the ascension of statistics as a "valid" form of reasoning in the early 20th century has altered the course of science. And while "The Lady Tasting Tea" is enjoyable, it doesn't make for compelling page-turning either as philosophy of science or as biography.

I can excuse Salsburg for not going into more depth mathematically, but I still felt like many of the concepts were dangled just out of reach. Although the role of statistics in science is the focus of the early part of the book, this historically fascinating and still relevant topic is never fully developed. Salsburg never really gets down to the subtle job of teasing apart determinism and non-determinism; deterministic theories and statistical ones; absolute truth and pragmatism; and cognitive agents, theories and reality. A deeper inquiry would've inevitably led to the philosophical context of logical positivism and empiricsm, with philosophers such as Popper, Russell, and later Quine struggling with statistics and its relation to induction as the underpinning of scientific theory.

The biographical sketches, which form the bulk of the book, provide a straight-line historical organization to the subject matter. Although the section on Pearson gets off to a good start, the later sketches are much weaker and read like encyclopedia briefs. It almost feels as if the author gave up halfway through the book when he saw the enormity of the task he had set for himself. By way of contrast, James Gleick managed to convey a real sense of the the interplay of theory, personality, publication and conferences in the much larger book "Genius", though having Richard Feynman as a subject made Gleick's job substantially easier. Paul Hoffman also painted a moving picture of Paul Erdos, the peripatetic mathematician in the shorter "The Man Who Loved Only Numbers". Thus writing a compelling book about science and scientists clearly isn't impossible. In Salsburg's defense, perhaps there just weren't as many colorful personalities in statistics as in physics or mathematics; one gets the impression of a rather dour bunch on the whole.

I am pleased that someone in the area of statistics has followed the example of those authors who have attempted to outline the history - and to some extent the philosophy - of math (Eric Bell) and physics (Timothy Ferris). While statistical methods form the basis for so many areas of cutting-edge research today, when it comes to the general public, statistics is probably one of the most unappreciated sciences (read: lies, damned lies, and statistics). Additionally, those in the statistical sciences (I'm a graduate student in statistics) many times overlook the development and hence the philosophy and thinking behind many of the methods they use. Unfortunately, statistical methods are thus sometimes put to use by someone who is intent on using a tool without considering the implications or liabilities of using that tool.

Hence, the topic and motivation of this book are needed. But I wonder whether the book has an appropriate audience, especially one outside of the statistical sciences. As I read through the book, I had to wonder whether I would have understood a single thing had I not had the background in statistics. True, the author avoids use of mathematical formulas. But telling the reader that so and so developed goodness of fit and that so and so developed maximum likelihood is not going to be very helpful if that reader doesn't know conceptually what these things are. To be fair, the author does go into more detail than that, but I still wondered whether the general public would get any of it.

As a statistician, I did enjoy reading the book and I obtained a good amount of cohesive information that would have been very difficult to find elsewhere. My only complaint about the book is that it was not more mathematically or philosophically rigorous.

But then I don't think such a book can provide both rigor to one audience and ease to another. I'm just not quite sure which audience the book was intended for.

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!

I am a high school math teacher who has long thought that statistics is more important in the curriculum than calculus. This book is one I can now recommend to colleagues and parents who question the importance of the subject. (Usually these adults had miserable experience with statistics courses in the past where formulas and computation, rather than understanding, drove the class.)
This book accomplishes two things. First, it conveys the development of statistics in the 20th century as the science of science - i.e. how experiments and surveys form the basis for knowledge and how to evaluate that knowledge. Second, it puts a human face on those who contributed to the field. The author's stories of Fisher vs. Neyman are wonderful. I especially appreciated how Salsburg relates the role of women in the field. They were often would-be mathematicians who were directed into statistics as a more "appropriate" field for women. Fortunately, as government use of statistics expanded, women civil servants were often already in place to provide quality analysis.
This book will probably not be widely read, but it should be...especially by scientists, journalists, and teachers.

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 th