A very disappointing book. The author gives us exceptionally long-winded, boring lead-ups to what seemed to be promising insights, only to provide the most cursory discussion of those points. Also, most of the points in this book are well known and well documented, e.g. that the scale of a graph makes a big difference in the impact of the data being presented. Where were the editors??? ( )

I'm not really sure for whom Seife wrote this book. The majority of people who like math and/or statistics will already be very aware of most of the statistical concepts that Seife introduces in his book: significant digits, the importance of looking closely at how axes are labelled, appropriate population sampling and correlation vs. causation. And the people who don't like math won't voluntarily read a book on math. So that leaves...I don't know: people who like math but are bad at it? Middle-schoolers? And unfortunately, this book won't work great for those people either, because rather than using the actual names for the mathematical concepts, like I did, Seife makes up terms so that if this is your first exposure to the concepts, you won't actually be able to communicate about them or google more about them. I think my turning point with Seife was in an appendix about the difference between sensitivity and positive predictive value, where I was originally annoyed that he didn't name-check Bayes and then realized that he also didn't mention sensitivity or positive predictive value in the entire appendix even once! This appendix was literally about how just knowing the sensitivity of a test without knowing the prevalence of disease results in not being able to predict the positive predictive value and he didn't use the names for a single one of those concepts.

I found the latter half of the book more interesting: Seife largely moves away from mathematical concepts and investigates political hijinks, such as the Franken election, Bush v. Gore and gerrymandering. It doesn't really add to numeracy, nor have that many striking examples of "proofiness," (except that humans can't count numbers to 6 digits worth of significant figures, which hopefully most people intuitively know) but it is interesting.

Overall, it's not a bad book. I might give it to a child who was interested in math, but I don't think most adults will enjoy it very much. ( )

This book starts out feeling much like the book: [b:How to Lie with Statistics|51291|How to Lie with Statistics|Darrell Huff|https://d202m5krfqbpi5.cloudfront.net/books/1388200174s/51291.jpg|415346]. But as I read more, I realized there is a big difference. How to Lie With Statistics is much more innocent. [b:How to Lie with Statistics|51291|How to Lie with Statistics|Darrell Huff|https://d202m5krfqbpi5.cloudfront.net/books/1388200174s/51291.jpg|415346] is useful, it tells a person how to understand misleading statistics. This book concentrates on current areas of deception such as swinging elections and conviction of innocent persons. For a basic understanding of what to watch for in statistical claims found in news and articles, I suggest the other book is more useful. ( )

A marvelous account of how people use numbers to their advantage. With a bit of hand-waving and mumbo-jumbo, you can use numerical data to prove almost anything you want. Bend numbers to your will like Humpty Dumpty with words and you can do some sinister things. The author splits this book into eight chapters with each one explaining how 'Proofiness' is ruining that particular area of inquiry.

The main idea is that humans are bad with numbers. Rather than calculating risk and such things, we are terrible at probabilities and figuring. We tend to rely on rules of thumb and heuristics that generalize far too much. So by attaching a number to something, even if that number is spurious or wildly untrue, it makes it seem true and hard to dispose of. The book starts with a relatively old example; remember the McCarthy trials of the 1950s? Senator Joseph McCarthy of Wisconsin pulled a number out of his ass that he said represented the number of Communist spies in the State Department and claimed to have proof. The number gave the claim credence and seemed true, but was not. This began the Red Scare or it could have just been a part of it. Another example given is that of the Subprime mortgage lending bubble. Since humans are bad with risk, there are people that take advantage of that fact and sell bad stocks and stakes in companies.

So anyway, I thought this book was great. It went by very quickly and was quite fascinating. It was also aggravating, since a lot of this 'Proofiness' results in unfair things happening. ( )

See the NY Times review http://www.nytimes.com/2010/09/19/books/review/Strogatz-t.html?_r=4&hpw