The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty

This book is difficult to pigeonhole, but I would describe it as an insightful, somewhat irreverent examination of risk and risk analysis from a "right-brain" perspective, rather than a statistics book for non-statisticians. (The irreverent part stems from the author's disregard for what he refers to as "Steam Era Statistics.") Rather than starting with statistical theory, equations, proofs, and Greek-alphabet soup, the author uses clever and engaging examples to clarify the principles. If there is an "organizing principle" for the book, I believe the author sums it up in this statement: "My position is that decisions are made using the seat of the intellect at one extreme and the seat of the pants at the other and that the best decisions are those upon which both extremes agree." Speaking from the standpoint of one who has a personal and professional interest in making abstruse mathematical and statistical concepts understandable to "right-brain thinkers," I am happy to report that this book lived up to my expectations.

As other reviewers have noted, this is an exceptional account of what you need to know about statistics, without any of the boring or intimidating stuff people like to layer on. No combinatorics, no measure theory, no calculus. It's clear and entertaining, with helpful links to simulations and animations on the web. The illustrations are amusing and useful, the overall production quality is significantly better than similar books. The writing is skillful and lively. Experts (and I consider myself one) will learn some new things and, more important, learn effective ways to explain things they already know. Novices will learn what they need, and sharpen their thinking skills. People in between will unlearn a lot of nonsense, and replace it with good stuff, and get the confidence to ignore self-proclaimed experts with dense jargon and impenetrable formulas. Key concepts are reduced to easy-to-remember "mindles." There are examples from most areas of finance, including some quite advanced, and business; with a few from other fields. What more could you want? Well, one more thing, but it's impossible. The author is the son of Jimmie Savage, and I consider his The Foundations of Statistics one of the great accomplishments of human thought. It was his intellectual precision and genius, and those of a few other people, that allows statistics to be made simple. Before that work, people were impossibly confused about the basics. It would have been nice to see that acknowledged instead of ridiculed. However, I realize so many people are traumatized and intimidated by statistics that it takes a little iconoclasm to motivate them. But why did it have to be from Jimmy Savage's son? Why not someone whose parents were killed by an overmathematical analysis that overruled common sense? The sections on finance, my specialty, are quite deep. His explanations of options, portfolio management and risk are excellent. It reads at the level of Kiplinger's Personal Finance magazine, but it makes the points of more intimidating authors such as Benoit Mandelbrot, Nassim Taleb and Kent Osband. His accounts of business management issues a bit more superficial, but still excellent. I do have a few specific gripes, that will bother no one but nerds. He uses "Monte Carlo simulation" to mean simulation of a random event. This is a near-universal error. Monte Carlo means creating randomness that doesn't exist to get a deterministic result. It matters because anyone can simulate a random event, and it's an obvious idea. Monte Carlo can wonderful, unexpected answers to seemingly intractable questions, but it requires a lot of precise mathematics to do correctly. Another gripe is he defines "Value-at-Risk" (VaR) as just a percentile. There is much more to VaR. For example, he estimates the distribution of return on movie investments by resampling from 28 past movies, which includes one blockbuster. Someone familar with VaR would realize that one observation is not enough to re

If you've had formal classes in statistics and probability (as I have) you will absolutely get a new grounding in the subject through this great survey of typical projects that give bad direction because the modeler simplifies by "using a number" rather than a distribution. To paraphrase (because I'm not going to find the exact quote) -- you can tell most about a modeler's assumptions by looking at what's NOT modeled -- as in Savage's explanation that one mortgage lending firm's model could NOT accept a negative percentage for the change in housing prices. Great back stories on the mathematicians that influenced Savage and how work was regarded and extended over time. Savage has a website, [...] where he shares several interactive examples and also provides some Excel files for download (some of which contain the @RISK Excel Add-ins). Frankly, the website is somewhat less than polished and the Excel files could be a little more user friendly (but hey -- people always say that about my Excel models and a little complexity helps filter out the unmotivated, right?) but they are a great addition to the book. Very up-to-date and apparently Savage was adding new material right up to press time related to other author's work and current events. A number of typos that I'm sure will get caught in a second printing. But bottom line, a must-read for anyone involved with forecasting, ROI, portfolio optimization.

In The Flaw of Averages, Sam exposes the epic failure of using single number value averages in models. His cure is an invention called probability distribution strings, where thousands of numbers populate a single cell, to create visual, interactive simulations, which can inform business decisions with unprecedented clarity. This book is a fun read, and written in everyday language, so even if you're not a quant, you will still benefit from Sam's genius.

Sam Savage has written a book that reveals what is behind a simple flaw in so many management decisions. This book leads the manager who is used to accounting-style, point-estimate thinking to the world of thinking with probabilities. His writing style is light (sometimes even funny) but the content is meaty. Savage criticizes what he calls "steam era" concepts of statistics which most stats courses seem stuck in and introduces decision making under uncertainty in a way that is much more welcoming than most books on this topic. I suspect that if more people had a professor like Sam Savage as their first mentor on statistics, there would be far fewer people with bad memories of that course. His approach is all about avoiding intimidating terminology and getting hung up on esoteric concepts. In particular, he explains the concepts of Monte Carlo simulations in a way that might just get the reader excited about the power of the tool. He is not only an expert in MC simulations himself (he has developed many new innovations in the method) but is also an expert in how to explain it to a wide audience. This is a book written for laymen with enough interesting insights to engage even the most scholarly professional.