The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty [Englisch] [Gebundene Ausgabe]

So, let me start by saying that I think this book had a ton of potential. I couldn't put it down after reading the first several chapters. The author is witty, clearly very intelligent and has thought a lot about his subject matter. He clearly describes the problem(s) through the use of clever analogies. My favorite is his comparison of using averages to that of a drunk walking down the highway. On average, the drunk's path is along the center line - but on average, he is dead. But, the book in my opinion has some serious flaws.

The essence of the book is that you should not use an average number in your predictions / forecasts / etc. Rather, you should use a distribution (e.g., simulations) instead. More specifically, he recommends the use of "Probability Management" which is his brainchild - basically boiling down to sharing probability distributions that are "certified" by designated specialists in the organization. This ensures that your work takes interrelationships into account, whereas separate simulations might miss the boat.

The main problem with the book, in my opinion, is that he talks far too much about the problem and far too little about the solution. He spends chapter after chapter talking about where the problem exists (or previously did exist), but doesn't give much in the way of details for the alternative. Even when the alternative is discussed, it is rarely in enough detail to glean any real kind of information about the solution to the problem other than a cursory overview.

The book also includes a lot of superfluous chapters that don't seem to fit with the book. For example, chapter 35 is about World War II statisticians (one in particular) that used a clever trick to figure out how many German tanks were created based on those captured by the US Army. While the chapter was interesting, I'm not exactly sure why it was in the book - maybe leading up to the next chapter regarding the war on terror? In the end, it seemed to me that there was too much fluff.

I would have given the book a lower rating, but the author was too funny and interesting to go that far. Also, I think it depends on the type of person you are - I'm a doer, a self-taught programmer and I like solving problems and implementing solutions. A different personality may have enjoyed the book differently and not minded the things that I did.

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.

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 reliably estimate either the probability or potential size of blockbusters; and those factors are very important to the result. So she would resample among the other 27 movies, and call that the distribution inside the 95% VaR limit. To estimate what might happen outside the VaR limit, she would look at a much longer record of movies to get enough observations of blockbusters. This is necessarily a judgmental process (it's called "stress testing") because she's bringing in less comparable data. The end result is a range of profits where normal conditions apply and you can make reliable probability forecasts; you optimize in this range; and a much larger range where you have only qualitative guesses about probabilities; you create plans to maximize probability of survival in these ranges.

I cannot think of a person who will not benefit significantly from this book. It won't make people forget his father (thankfully) but it's one of the few books worthy to be on the same shelf.