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Uncertainty: The Soul of Modeling, Probability & Statistics, by William Briggs
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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.
- Sales Rank: #70385 in Books
- Published on: 2016-06-30
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .69" w x 6.14" l, .0 pounds
- Binding: Hardcover
- 258 pages
Review
“[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.
About the Author
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.
Most helpful customer reviews
7 of 7 people found the following review helpful.
A sorely needed foundational work in probability and statistics
By James Arruda
This book is, in many ways, two books in one. The first is an excellent review of the philosophy of uncertainty, probability, and causes. This portion would stand on its own quite well, and an astute reader would be able to draw out the second part mostly on their own from its foundations. The key to this part of the book is understanding that probability is not ontological. Along the way, an Aristotelian view of causes is given which provides a necessary context to help distinguish probability from cause. This may be very new or different to some readers, but it's quite necessary and edifying.
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!
6 of 7 people found the following review helpful.
Philosophy, not mathematics
By Dietlbomb
This is an excellent overview of the philosophical foundations to probability and predictive statistics. This a book about philosophy behind probability and the use of evidence to calculate probability. It is not a book about how to calculate probability; in fact, the author argues repeatedly that often calculating probability is impossible when the given premises frequently don't lend themselves to quantification.
What is it all about? This: scientists create statistical models of some proposition of interest and apply some standard statistical test to determine whether the proposition has a sufficiently small p-value, which shows that the scientists were on to something. If you aren't a trained scientist, the previous sentence probably reads like nonsense. Briggs shows in his book that it actually is nonsense. The standard statistical methods are useless to anyone trying to answer the actual question scientists ask: what are the causes of the effects we observe?
Probability models don't give us the answer to that question either, but they do give us something useful: they quantify the uncertainty in our predictions of observables based on premises (both contingent and necessary), a model (different models imply different probabilities!), and evidence. Using probability in this way prevents us from dwelling on such imaginary creatures such as trendlines, p-values, and spread-parameters, and makes us focus on real things: observable evidence. If you aren't familiar with this argument, Briggs's book will explain how probability models fit into a coherent system of knowledge.
As a control systems engineer, I was familiar with particular predictive probability models useful to my field, but I wasn't familiar with their epistemological foundation.
This book is quite useful for practicing scientists and engineers, as well as for discriminating newsreaders.
A useful sequel to this book would be a book of examples from various fields of study: engineering (say, signal processing or control engineering), social science, physics, employing predictive statistical methods in a way that is consistent with the principles laid out in this book.
Another useful sequel would be some sort of guidebook for practicing social scientists, because so much of what they produce is abject junk and they really need a corrective as soon as possible. The gatekeepers of academic publishing should have the authority to prevent junk studies from being published, but they need something to point toward as an example to follow.
A few niggles:
Readers of the author's blog will be familiar with most of the material in this book; almost everything in the later chapters appears on various blog posts Briggs has posted over the years. However, the book has some added value for merely putting these ideas in a coherent order.
The examples Briggs uses are difficult to understand. The transition from philosophical argument to mathematical examples is jarring, which is a common problem in any book that uses mathematical examples. Getting this right is really hard, but reader feedback will help calibrate these sections for a revised edition.
For example, in Chapter 10 Briggs describes the practice of employing predictive statistics to better quantify the uncertainty of predicted cancer rates given knowledge in concentrations of PM2.5 (some sort of particle). There are several charts showing how employing the proper predictive method helps account for uncertainty that is ignored by the standard statistical methods. However, he elides over all the math, leaving the reader to guess how this was accomplished. Maybe the references have more to say about implementing the predictive methods. Briggs acknowledges that this isn't an algorithm cookbook, but a little more detail might help the reader follow the arguments.
Briggs often blames (tongue in cheek) his enemies for typos on the blog. His enemies were active in the book's editorial process.
In addition to being a statistician, Briggs is also a polemicist. He removed almost all his political views from this book, so they shouldn't distract the reader.
0 of 0 people found the following review helpful.
Unique
By Roman Pohorecki
2500 years of humanity efforts to develop methods enabling us to formulate a valid statement is presented concisely and clearly. This is not not a statistics text, it is not a treatise on philosophy of science or logic. This work is like nothing I have seen before, an excellent combination of the above, indeed the "the soul of modeling, probability ...", presented with passion and accessible to everybody. Illuminating ! I hope that the author will find enough energy to prepare the expanded edition of this remarkable book. Unlike one of the reviewers I am convinced that the book is not overpriced, quite the opposite - the bibliography is worth more ! If you are a medical doctor, engineer, scientist, economist, sociologist, psychologist, teacher, etc., or if you are just interested, do yourself a favor and red this book. You will be better equipped to distinguish a chaff from the grain.
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