- Hardcover: 258 pages
- Publisher: Springer; 1st ed. 2016 edition (June 30, 2016)
- Language: English
- ISBN-10: 3319397559
- ISBN-13: 978-3319397559
- Product Dimensions: 6.1 x 0.7 x 9.2 inches
- Shipping Weight: 1.2 pounds (View shipping rates and policies)
- Average Customer Review: 15 customer reviews
- Amazon Best Sellers Rank: #160,738 in Books (See Top 100 in Books)
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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 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.
Top customer reviews
This lead us to Briggs' new book "Uncertainty - The Soul of Modeling, Probability and Statistics". It is a deep philosophical treatment of probability written in a plain language and without the interference of unnecessary math. This makes the book accessible to most university students. The books "Probability Theory: The Logic of Science" by E.T. Jaynes and J. Pearl's "Causality" are the ones that have influenced my thinking most profoundly. Until now. Briggs explains why subjective, Bayesian or frequentist interpretations of probability are somewhat unfortunate and argues that the fundamental view should be to see probability as the extension of logic to the domain of uncertainty. I have met this view in other places before, but this is the first comprehensive treatment I have read. He also argues that all probability must be conditional - again, it is not the first time I have seen this view, but the first time I have seen a deep analysis of why that must be so. Now, it may not sound like much, but it really is. It has already allowed me to get a fresh perspective of one of my AI research problems that has plagued me for years.
Have you ever speculated what randomness really is? This book will tell you. Is there a mathematical definition of falsifiability? Oh yes. Do you ever wonder what the relationship is between probability and causality? And what is the role of statistical significance testing in relation to causality? A few years ago I read "The Cult of Statistical Significance" by S. T. Ziliak and D. N. McCloskey which is basically arguing against certain statistical practices while emphasizing the focus on effect sizes. Briggs' book stand out because his analysis is much deeper (mathematically, philosophically) and because he goes much further by proposing that both p-values and relative risk should be abandoned, although he dislikes p-values the most. To read Fisher's old gibberish, that led to this sad situation, is simply astounding.
I also have a little critique. First of all, I had hoped there was a section on the notion of "unbiased" estimators, but maybe Briggs can add that to the second version. Secondly, there are brief discussions of machine learning algorithms for causality. The reader could get the impression that people in this field think they can prove causality. If so, that is certainly not the case. From the little I know, then they always assume some kind of faithfulness of the distribution, or they take the graphical model as inductive knowledge (e.g. in the case of Pearl). Of course, the problem is, as pointed out by Briggs, that once the techniques get in the hand of less rigorous scientists, then they tend to forget that and immediately think causality has been proven. Briggs is kind to remind us that there is a difference between conditional and necessary truths, and once you start to
assemble all your assumptions, the conditional truths may quickly become very uncertain.
In general, then this book should be relevant for anybody working with probability models and anyone consuming the output of such models. That's a lot of people, including almost each and every scientist and university student. If you are a journalist, then read it too. It will give you a much better basis for accessing the nature and validity of all the research fluctuating in the media.
This lead us back to the question I was asked by the professor 7 or 8 years ago: "why do they work?". I said that I didn't really have a book on that (and I have many books). I think you might have to get the original paper(s) and see what's in them. Then we discussed his problem a little, and I suggested a chi-square test and sent him out the door with a bunch of books, among them one with the reassuring title "100 Statistical Tests". Today I would simply have given him Briggs' book and said: they don't work and here's why!
Thorsten Jørgen Ottosen, Ph.D.
Director of Research
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!
Look out for Section 9.7: "Die, p-Value, Die, Die, Die"!
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