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anna_hepworth's review against another edition
This book did not work for me, except to explain to me why economists do some Really Weird things in their modelling. I say this as a statistician and ex-systems modeller. Some of the concepts are overly simplified to the point of being wrong, at least one of the methods that is touted as better than the statistical approach is one that I teach people not to trust because it is easy to lie about the numbers with, and the author attempts to claim that Excel is the model common tool for modelling (maybe for economists, but no-one else I know that does models uses Excel (any more) for anything except the most basic of prototyping).
The harping on of 'don't use the statistical language, use another term instead' frustrates me. This is how I ended up having to teach multiple different sets of terminology in the basic stats classes, because each discipline wants to impose its own thinking.
Some of the early parts of the book were useful for me, because it gave me a framework for how to explain some of the things I do to economists. I suspect that there are lots of people who would learn something from it, but I suspect I'd recommend something instead of this one, depending on what I was asked about
The harping on of 'don't use the statistical language, use another term instead' frustrates me. This is how I ended up having to teach multiple different sets of terminology in the basic stats classes, because each discipline wants to impose its own thinking.
Some of the early parts of the book were useful for me, because it gave me a framework for how to explain some of the things I do to economists. I suspect that there are lots of people who would learn something from it, but I suspect I'd recommend something instead of this one, depending on what I was asked about
ashrafulla's review against another edition
2.0
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 come on, calling it a SLURP!? How about a realization? That's extremely easy to understand. The hard part of probability is not its basic terminology; it's the ludicrous laws governing the distribution of the sum (and thus any function) of two variables. It turned me off at the beginning and at the end.
His attempt to convert people from parametric distrbutions (Gaussian, chi-square, etc) to nonparametric distributions (using raw samples) is noble. In fact, in many places that turn has already been made. However, he is forgetting that nonparametric distributions are also estimators with massive variation as compared to parametric ones. If you know the data is Gaussian, just use the Gaussian PDF. You get a much better estimate. That's why a lot of statistical research uses hybrid approaches: non-parametric when necessary, parametric when possible.
The good part of the book, the 2 stars in it, are the examples of non-linear payoffs. For example, the licorice drug was a fantastic description of how to understand that distributions do not work like numbers. They don't add nice, they don't do anything nice. I would focus on these examples as the best and brightest and most illuminating parts of the book.
Savage does a salesman's job with respect to all the analytics software and spreadsheets, but I do wonder if the spreadsheet as a data device is in need of dismissal. With the accessibility of simple coding structure (Python & MatLab for example), why not just keep the data in whatever format, have a reading function, and then work in code? The only advantage spreadsheets have is some visualization aspects, but I'm worried that spreadsheets force bad behavior. Maybe that was the idea behind the differing GUIs from Shell and Merck.
Anyway, this book is a quick read if you want to understand at an intuitive level as to why using means without appropriate modeling is a bad idea. His message to use Monte Carlo simulation is completely accurate and is the key takeaway. I would just gloss over some of the silly terms and the weird Probability Management pitches.
I understand, statistics is in my expertise, so I'm a bit biased. But come on, calling it a SLURP!? How about a realization? That's extremely easy to understand. The hard part of probability is not its basic terminology; it's the ludicrous laws governing the distribution of the sum (and thus any function) of two variables. It turned me off at the beginning and at the end.
His attempt to convert people from parametric distrbutions (Gaussian, chi-square, etc) to nonparametric distributions (using raw samples) is noble. In fact, in many places that turn has already been made. However, he is forgetting that nonparametric distributions are also estimators with massive variation as compared to parametric ones. If you know the data is Gaussian, just use the Gaussian PDF. You get a much better estimate. That's why a lot of statistical research uses hybrid approaches: non-parametric when necessary, parametric when possible.
The good part of the book, the 2 stars in it, are the examples of non-linear payoffs. For example, the licorice drug was a fantastic description of how to understand that distributions do not work like numbers. They don't add nice, they don't do anything nice. I would focus on these examples as the best and brightest and most illuminating parts of the book.
Savage does a salesman's job with respect to all the analytics software and spreadsheets, but I do wonder if the spreadsheet as a data device is in need of dismissal. With the accessibility of simple coding structure (Python & MatLab for example), why not just keep the data in whatever format, have a reading function, and then work in code? The only advantage spreadsheets have is some visualization aspects, but I'm worried that spreadsheets force bad behavior. Maybe that was the idea behind the differing GUIs from Shell and Merck.
Anyway, this book is a quick read if you want to understand at an intuitive level as to why using means without appropriate modeling is a bad idea. His message to use Monte Carlo simulation is completely accurate and is the key takeaway. I would just gloss over some of the silly terms and the weird Probability Management pitches.
statman's review against another edition
3.0
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.
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