Naked Statistics MP3 CD – SACD, 27 May 2014

In the wrong hands, statistics can be a dangerous thing. In the right hands, there's still no guarantee that the statistics you're reading in your newspaper or on your screen are much more trustworthy. In Naked Statistics, Charles Wheelan provides a little bit of a toolkit to give the uninitiated a fighting chance of at least recognising the potential pitfalls in the analysis they're reading and to ask some smart questions about its foundations.Wheelan lays out his stall pretty early, making it clear that he's not a numbers for their own sake person. Numbers to him are only of any interest if there's a point, and he proceeds to explain why statistics are, in the final analysis, potentially a useful and friendly set of devices for explanation and, quite importantly, identification of potentially the best way to respond to a problem or need in government or business. In generally clear language he explains the basics of descriptive statistics, correlation, probability, polling and regression analysis, as well as outlining some common pitfalls and how they may be circumvented.The writing style is clear and lively, and the examples he uses are practical and engaging, although the sports examples are very much oriented to the American reader, with abundant reference to Lebron James, quarterback ratings and at bat averages. In some ways, from that point of view it's something of a complement to Scorecasting, a book that applies principles of behavioural economics to sports (explaining, amongst other things, the reason for "Fergie time" and home field advantage).But it isn't by any means all sports. One of the examples he dwells on is the use of Value at Risk (VaR) in banks, one of the infallible tools that told bankers that subprime mortgages would not pose a problem to their institutions, let alone precipitate a global financial crisis that would persist for six years and counting. The BBC documentary series Bankers focused on VaR in one programme, showing how excessive faith in the model had brought down MF Global, ably abetted by the hubris of former Goldman Sachs CEO and New Jersey Governor Jon Corzine, and how selective use of the model led to the infamous "London whale" debacle at JPMorgan.Ironically, VaR was developed at JPMorgan, and one of the "quants" responsible, Till Guldimann, was interviewed for the BBC series, opining that VaR was good for measuring risk, but not for managing it. Wheelan reveals that actually it wasn't even much good for measurement, given that the data underlying the model was based on the boom period between the eighties and the turn of the millennium. He compares it to a faulty speedometer, worse than no speedometer at all, giving confidence where none is due.Naturally there are a couple of issues.His comparison between the Businessweek phenomenon, where business leaders featured on the cover for receiving high profile awards are guaranteed to be about to fail big time, and the Sports Illustrated effect, where athletes on a winning streak featured on the cover are about to hit a dry spell, one due to overconfidence, the other possibly because the winning streak was just a matter of luck, is valid enough. But he forgets to mention that sometimes behind the athlete's winning streak is a lifetime of practice, and the fall often due to injury (let's hear it for Carson Palmer, or in English Michael Owen).Though mostly explanations are clear, I'm still not sure his distinction between "precision" and "accuracy" work that well, certainly for me.More importantly, in one instance (p198 in the hardback) the term "dependent variable" appears where "independent variable" is intended, and he doesn't explain very well why it's the 99th percentile that represents the top 1%, not the 100th percentile (a little chart might have helped there, perhaps).Those aside, however, I definitely found myself wishing I'd read this book before I studied econometrics. I think the veil would have lifted much earlier.

Naked Statistics is a good way to remind yourself what statistics is about, or if new to the subject, get a solid grasp of the basics. It is a fine complement to a dry textbook, in that it covers the groundwork in a clear, approachable and entertaining way that is not overly mathematically demanding. Appendices delve deeper into theory and can be read or ignored as the reader wishes.The first two thirds of the book is particularly good, breezing competently through key statistical concepts up to and including the Central Limit Theorem.Many people may be drawn to the book because of the growing importance of 'big data'. Wheelan takes this topic on board with a focus on regression analysis, and is not afraid to discuss the pitfalls as well as the benefits of the more abstract 'darker' arts of statistics. However, given the choice between a candid acknowledgements of the fundamental limitations of statistics and an uncomplicated view that 'as long as its done well all will be fine', Wheelan goes in the simpler, more positive direction, even when cheerfully supporting claims that over half of the top-flight peer reviewed scientific papers that draw conclusions from the techniques he proposes are likely to be wrong.Instead, Wheelan argues that brilliant statistical research simply requires brilliant researchers (guess who?) - and that brilliance is not about being good at the maths, but about a having a creative and intuitive grasp of what works. There are two problems with this. One is that observant readers may well spot flaws in the exemplars Wheelan presents as brilliant. The second (and more important) is that the power of statistics is meant to be its ability to reveal insights that are drawn entirely objectively, yet it is clear that many mistakes in statistical research are due to failings in the researchers' subjective and interpretive skills - in other words, the maths disappears - advanced stats is a matter of judgement (so why not rely on judgement and abandon the somewhat bogus claim of objectivity?).Consequently (and slightly disappointingly), Wheelan's concluding chapter is all about the amazing contribution statistics will continue to make to solving the world's most pressing problems, rather than a more reflective assessment of its strengths and weaknesses.All this said, this is a likeable and workmanlike book that treats a potentially dry subject with significant flair.

This has been sitting on my wishlist for a while and bit the bullet and bought it when it was recommended by a couple of Professors for a "friendly refresher" of the basics of statistics, to accompany more advanced reading.I wasn't expecting much, but Wheelan blew me away with the awesomeness which he squeezed into this book. If it isn't already, this should be on the reading list of every undergraduate at University. Heck, it should be on EVERYONES' reading list! It starts with an introduction to the basics and progresses into more advanced material. All along the way, he explains all of the concepts extremely well and uses examples to get the point across. It's a shame a lot of his examples are Americanized, but you still get the point he's trying to make.As a graduate student, I kept up with this and found it a useful refresher and something which I can revisit to clarify topics in the future on a couple of the more advanced points. But, being honest, this is approachable for anybody with an interest in statistics and its usefulness in everyday life. If you have a really basic knowledge, you might have to re-read some of the concluding chapters once or twice, but Wheelan writes very well so I wouldn't expect this to be much of a chore.Just to conclude, it's a shame that more statistics book aren't like this. He makes a terrifying subject A LOT more approachable and something (SHOCK!) that we can find fun in! Wouldn't hesitate to recommend to anyone.