Uncertainty: The Soul of Modeling, Probability & Statistics 1st ed. 2016 Edition
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“Briggs, an adjunct professor of statistics at Cornell University, cautions his readers to carefully examine the uncertain reliability of such conclusions when these tools are used. His challenging premises are thoroughly supported by philosophical explanations as to why these traditional approaches need to be questioned. … Briggs provides fully fleshed out reasoning, impressive support, precisely worded insight, and graphical illustrations, as appropriate, to justify his stand. … Summing Up: Recommended. Upper-division undergraduates and above; faculty and professionals.” (N. W. Schillow, Choice, Vol. 54 (6), February, 2017)
“This is a book about probability and probabilistic reasoning. It is more philosophy than mathematics, but it does have mathematical content and it relies in some measure on mathematical reasoning. … This book is worth a look by anyone who teaches probability and statistics.” (William J. Satzer, MAA Reviews, August, 2016)
“[This book] is not for sissies, true, but its clear-headed (i.e., Aristotelian) approach to the subject of truth (which, in the end, is what exercises in probability and statistical analysis are all about, notwithstanding what they tell you in school) is refreshing: a long, cool drink of plain speaking about intellectual topics that, in these hot and humid days, is as enlivening as it is enlightening.” (Roger Kimball, The New Criterion's Critic's Notebook, newcriterion.com, August, 2016)
“This book has the potential to turn the world of evidence-based medicine upside down. It boldly asserts that with regard to everything having to do with evidence, we’re doing it all wrong: probability, statistics, causality, modeling, deciding, communicating―everything. … the book is full of humor and a delight to read and re-read.” (Jane M. Orient, Journal of American Physicians and Surgeons, Vol. 21 (3), 2016)
From the Back Cover
This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance".
The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models.
Its jargon-free approach asserts that standard methods, such as out-of-the-box regression, cannot help in discovering cause. This new way of looking at uncertainty ties together disparate fields ― probability, physics, biology, the “soft” sciences, computer science ― because each aims at discovering cause (of effects). It broadens the understanding beyond frequentist and Bayesian methods to propose a Third Way of modeling.
- Presents a complete argument showing why probability should be treated as a part of logic
- Broadens understanding beyond frequentist and Bayesian methods, proposing a Third Way of modeling
- Proposes that p-values should die, and along with them, hypothesis testing
William M. Briggs, PhD, is Adjunct Professor of Statistics at Cornell University. Having earned both his PhD in Statistics and MSc in Atmospheric Physics from Cornell University, he served as the editor of the American Meteorological Society journal and has published over 60 papers. He studies the philosophy of science, the use and misuses of uncertainty - from truth to modeling. Early in life, he began his career as a cryptologist for the Air Force, then slipped into weather and climate forecasting, and later matured into an epistemologist. Currently, he has a popular, long-running blog on the subjects written about here, with about 70,000 - 90,000 monthly readers.
- Publisher : Springer; 1st ed. 2016 edition (July 8, 2016)
- Language : English
- Hardcover : 277 pages
- ISBN-10 : 3319397559
- ISBN-13 : 978-3319397559
- Item Weight : 12 pounds
- Dimensions : 6.14 x 0.69 x 9.21 inches
- Best Sellers Rank: #1,480,854 in Books (See Top 100 in Books)
- Customer Reviews:
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All kidding aside, Dr. Briggs is determined to return the world to what can euphemistically be called a common sense approach to philosophy and statistics. I will grant him what he does not grant to his opponents—good will. But I do not understand how so obviously intelligent a man cannot understand that the world does not have to conform to common sense.
For example, when philosophers try to reckon with the enigmas of quantum physics they don’t dogmatize to the world what it must be like. They try to work with the knowledge that our brain has obviously not evolved to understand quantum sized reality. Given this, they try to speculate as best as they can in a critical community how mysteries like the seeming indeterminacy of the quantum wave function can be understood.
Briggs will have none of this. For him, it has all been explained 2,500 years ago by Aristotle. Chance can’t be a cause since it exists only in our minds and not in reality. This is a possible philosophical conclusion. But Briggs doesn’t present fair articulations of different perspectives. He makes jokes about how these theories are wrong by oversimplifying them and then showing how they seem counter to common sense. And then he dogmatically asserts his own Aristotelian based theories.
Similarly, his suggestions for a revolution in statistics are awful. Briggs seems to think that statisticians are determined to invent tests and models which can be understood only after long training in order to keep a monopoly on data analytics. No need for complex models! Use descriptive statistics or Briggs’s simplified methods. Nobody without a PhD understands those other techniques anyway.
In short, I thought I was buying a book that would try to resolve some of the open questions around the philosophical foundations of statistics. What I got was a diatribe against modern philosophy and statistics by someone who doesn’t even grant his opponents the right to be explained correctly before he attempts to refute them.
If you like simplistic, dogmatic and over-confident pseudo-philosophizing you will like this book. If you have any appreciation for how difficult philosophical and statistical questions can be to answer, if you think that most academics in these disciplines are honestly trying to improve their fields all the while knowing that they’re imperfect and that knowledge will advance in future ages then you’re better choosing a work that won’t inflame your ire like this exercise in archaic thinking.
The latter portion draws out the ideas from the former into the actual mechanics of probability and statistics, giving examples along the way. It is helpful for solidifying and demonstrating the first part of the book. The book is also suffused with personality, which may be a turnoff for some. I myself enjoyed it.
I'll briefly compare this book to 'Black Swan' because it is one the few recent books to address issues in modern probability and statistics. While 'Black Swan' was edifying in many ways, it was not very critical of the foundations of P&S. It took a segment of P&S work and said why methods are unsuccessful there. 'Uncertainty' takes us back to the first principles and could re-derive 'Black Swan' as a sequel, and do so from the perspective of causes and propositions, not merely from criticism of the lack of success in predictions (although there is certainly that!).
I knocked off one star because the book does wander a bit at times and feels less polished for flow. There are also several typos (readers of Briggs' blog will recognize the work of his enemies there) that an editor should have caught. Readers who are unsympathetic to the concepts in the book may also find themselves turned off by the personality. My advice is to push through, because the concepts are sorely needed and quite edifying.
Overall, this work does not disappoint. It will challenge you and open you to better ways of thinking. It'll also cause you to be frustrated to no end with modern statistics work, a feeling which is in short supply!
I thought that Briggs is funny and clear. These two attributes get him a long way forward in my book; certainly compared to other statisticians. I was unpleasantly surprised by the amount of typos, starting on page two, especially from an old school publishing house.
This book deserves a look from a very large group: anyone who consumes "evidence". Briggs will invite you to think and support you while you do it!