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The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty
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A must-read for anyone who makes business decisions that have a major financial impact. As the recent collapse on Wall Street shows, we are often ill-equipped to deal with uncertainty and risk. Yet every day we base our personal and business plans on uncertainties, whether they be next month's sales, next year's costs, or tomorrow's stock price. In The Flaw of Averages,
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Hardcover, 392 pages
Published
June 1st 2009
by Wiley
(first published May 13th 2009)
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Start your review of The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty
If you've read this book you'll find it quite ironic that I'm giving it a rating of 4. A real rating of the book would look like this:
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Sam Savage's flaw in the Law of Averages is roughly two-fold: dependence between variables and nonlinearity in the payoff. He forgets about the big one (small samples & non-Gaussianity kill the law), but that's not where I soured. I soured with his cutesy attempt to make probability accessible to everyone. I just don't like SIPs and SLURPs and whatever else is being used to just describe realizations of a random variable.
I understand, statistics is in my expertise, so I'm a bit biased. But ...more
I understand, statistics is in my expertise, so I'm a bit biased. But ...more
I was tremendously disappointed with this book. I got the false impression that it would focus on statistics in all aspects of life -- instead it is on discussing business cases, which makes it rather boring. In addition, statistical principles are discussed in very lay terms, without any of the actualy interesting mathematical details.
“Consider the state of a drunk, wandering around on a busy highway. His average position is the centerline, so the state of the drunk at his average position is alive, but the average state of the drunk is dead.” Turn on your computer, picture-google “The flaw of averages” and take a look at the brilliant picture that illustrates the quotation above. The insight behind this picture could have saved investors a lot of grief during financial history. Sam Savage is a consulting professor of
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Review: Opted not to read pages 173-243 (much of which focuses on applications in Finance and Real Finance - probably useful advice for investing, but very boring). The book has a few nice examples of how people overuse and abuse averages, and show how they can be nothing like what people actually experience (e.g., retirement funds over 20 years). However, the book becomes increasingly focused on finance and the stock market, and increasingly boring. Savage's writing style is slow and can put
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The average value disease is alive and well in management. I knew it before and I see it even clearlier after reading this book. Getting from taking mean values for truth to using distribution is a long way to go and Sam Savage gives you a lot of help.
Anyone who makes ANY business decision should know some statistics and could make good use of this book. It helps to know math, but you don't have to to get the points in the book.
I liked the chapters about options and the "mindles" most. The ...more
Anyone who makes ANY business decision should know some statistics and could make good use of this book. It helps to know math, but you don't have to to get the points in the book.
I liked the chapters about options and the "mindles" most. The ...more
Don’t just check the book’s score; check the variability of it too
This is an excellent and mostly easy to read book on common traps we fall in by guiding our decisions using a single figure, mostly the average of the variable of interest. The examples given make the flaws become vivid. Prof Savage introduces several memorable terms in English, making it more efficient to share ideas on uncertainty. The terms that contradicted the beginnings of the book are SIP and SLURP; difficult to remember. ...more
This is an excellent and mostly easy to read book on common traps we fall in by guiding our decisions using a single figure, mostly the average of the variable of interest. The examples given make the flaws become vivid. Prof Savage introduces several memorable terms in English, making it more efficient to share ideas on uncertainty. The terms that contradicted the beginnings of the book are SIP and SLURP; difficult to remember. ...more
A layman's version of the problems underlying the usage of the average without recognizing the variation in that average. Uses a lot of examples from financial investments and probability but there are a few other examples. Written in a more irreverent tone with many references to pop culture items such as movies. Good read if you don't understand the important of variation and uncertainty in statistics.
I was recommended this book by one of my Operations Research Professor's and found it to be pretty entertaining while educational. I think for someone who isn't familiar with Operations Research or Statistics would find this book much more insightful than me, regardless I still enjoyed it.
Insightful!
Fantastic Read! Whenever faced with uncertainties use distributions and simulations, not averages.
I work in IT - software development - and I've always had to deal with plans and estimates. This book explains with extreme clarity why the traditional way of using averages for such reasons is wrong in so many ways!
I already knew the saying that you're not meant to use averages as they are wrong half the time, but this book still opened my eyes on the real reasons behind it.
Some of my favourite ...more
I work in IT - software development - and I've always had to deal with plans and estimates. This book explains with extreme clarity why the traditional way of using averages for such reasons is wrong in so many ways!
I already knew the saying that you're not meant to use averages as they are wrong half the time, but this book still opened my eyes on the real reasons behind it.
Some of my favourite ...more
I'd recommend this book to anyone who works for a corporation that uses numbers.
In my professional experience, I've found that managers often ask for your best prediction on some uncertain outcome and when you try to explain all the underlying variables and distributions of those variables that could influence the outcome - they typically reply with an incredulous "Give me a number!" Without a sophisticated way to model or simulate the results, most analysts are left to finding the average of ...more
In my professional experience, I've found that managers often ask for your best prediction on some uncertain outcome and when you try to explain all the underlying variables and distributions of those variables that could influence the outcome - they typically reply with an incredulous "Give me a number!" Without a sophisticated way to model or simulate the results, most analysts are left to finding the average of ...more
Эта без сомнения умная книга написана столь же хорошо, сколько и утомительно. Глава за главой автор уговаривает нас не делать действительно распространенную ошибку: полагаться в наших планах на средние значения. Хотя тот, кто идет по середине дороги с двусторонним движением, не пострадает, пьяница, который идет по середине лишь "в среднем", будет "в среднем" же мертв. В общем, нужно думать не про средние значения (в среднем у каждого человека одно яичко и одна грудь, но это бессмыслица), а на
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loved this book. For my it was really eye opening to see so many ways that well intention-ed management direction could result in such poor operational outcomes. So many project management decisions and directions when designing new hardware, software, or physical products are related to asking for and executing strategy related to guesses about the future. The Flaw of averages spells out some common pitfalls in those guesses.
In my experience managing product design projects in for technology ...more
In my experience managing product design projects in for technology ...more
The book is a great introduction to some simple concepts of flaw of averages. The first half of the book does a great job in explaining the flaws of averages such as Jensen's inequality and some application of these flaws in real life. However the author keeps on repeating the same concept over and over again throughout the book in different domains such as finance, operations and trading. Most of the concepts covered in the book would be covered theoretically in the undergraduate statistics
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I usually called averages 'the curse of the average' because what does average really tell you without knowing the distribution shape? How often do people add averages (oops, unless the sample size is coincidentally the same).
The author advocates 'probability management' and creating a model that will run random tests using selected probability distribution functions.
The book's points are very good. Some one familiar with the 'theory of constraints' (like in The Goal, or It's Not Luck, or ...more
The author advocates 'probability management' and creating a model that will run random tests using selected probability distribution functions.
The book's points are very good. Some one familiar with the 'theory of constraints' (like in The Goal, or It's Not Luck, or ...more
Currently, a tectonic shift is occurring in the way statistics, probability, and simulations are being used for making decisions in the face of uncertainty. With this book, Professor Savage has captured a few of the core elements of this shift, has provided some history, and has pointed towards some future directions. Given how dry these topics can be, he has done well to make the book approachable and entertaining.
While the book meanders a bit, its principles are powerful. Interactive ...more
While the book meanders a bit, its principles are powerful. Interactive ...more
This is a great book - if you want to understand why humans are almost always wrong when they make estimates. Dr. Savage looks at the way statistics has been and is being taught and points out how the advent and now common use of the digital computer makes much of the current pedagogy obsolete. Likewise, he offers real, applied examples of how variance and risk assessment can and are being used.
I enjoyed this book enough that I've given copies to a number of colleagues, clients, and business ...more
I enjoyed this book enough that I've given copies to a number of colleagues, clients, and business ...more
Was excited about this one. It has some good points about how using averages is a good way to fail and how using probabilities and Monte Carlo simulations produce better results, but it just didn't wow me all that much. It came highly recommended from others and, possibly would have been better had I not read "How to Measure Anything" first. Seems to be a bit of overlap between the two. The author tries to steer us away from big words used in statistics to make people sound smart, but found that
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This was a super worthwhile read. It is a highly accessible read and is very thought provoking. Highly recommended. So much so that I'll read it again in short order to ensure the concepts are connected to the seat of my pants (a metaphor from the book, which you'll have to read to understand...).
I only gave it 3 stars because of the lack of structure. The book starts out great --- then meanders into interesting, yet not so cohesive sections. However, I really like the concepts presented and think there's quite a bit of value to this approach. I recommend (especially the first half) to anyone that deals with making decisions in the face of uncertainty.
This book was required reading for an MBA class. The information was good...relevant to the the class. However, I feel the information could be said fewer words...much fewer. If you are a numbers and statistics person you may find all the supporting evidence fascinating. I was wishing there were Cliffs Notes.
Woah,
It's an eerie yet comforting feeling reading people discuss this book. I had the chance of working on a research project and take a course by this professor. Good book, especially for the layperson, (which although we like to pretend we're not,we all are inside). Good basic concepts and the big take away is that life is a distribution, never settle on a number. :)
It's an eerie yet comforting feeling reading people discuss this book. I had the chance of working on a research project and take a course by this professor. Good book, especially for the layperson, (which although we like to pretend we're not,we all are inside). Good basic concepts and the big take away is that life is a distribution, never settle on a number. :)
This book is very intriguing, it is a challenge to understand and twice I stopped reading and tried to resume. I believe that the concept of averages and the concept of probability are very relevant to our everyday life. I wish I had more time for this book and plan to go back to it.
An interesting book about how much averages show up in our everyday world, and how often they are not a good number to follow. Not the best book about such things, the narrative is not as engaging as something like "Freakanomics". But the information about the stockmarket is very interesting.
Jul 14, 2010
Jeremy Karmel
added it
A good review of basic mistakes business make when doing market analysis. The basic insight is that profit at the average sales is not the same as the average profit given a sales distribution.
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“The five stages of model development. —Donald Knuth, Stanford computer scientist Knuth discovered that computer program development goes through five stages. These steps also apply to building models, and I rigorously adhere to them in my consulting work. 1. Decide what you want the model to do. 2. Decide how to build the model. 3. Build the model. 4. Debug the model. 5. Trash stages 1 through 4 and start again, now that you know what you really wanted in the first place. Once you realize that step 5 is inevitable, you become more willing to discard bad models early rather than continually to patch them up. In fact, I recommend getting to step 5 many times by building an evolving set of prototypes. This is consistent with an emerging style of system development known as Extreme Programming.2 To get a large model to work you must start with a small model that works, not a large model that doesn’t work. —Alan Manne, Stanford energy economist”
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