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Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics

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Audible Audiobook

Listening Length: 9 hours and 20 minutes

Program Type: Audiobook

Version: Unabridged

Publisher: Gildan Media, LLC

Audible.com Release Date: June 1, 2016

Language: English, English

ASIN: B01G61WNTW

Amazon Best Sellers Rank:

This book is a must if you invest or need to dispassionately evaluate many kinds of evidence used in studies involving mathematics including graphs. If you take nothing else from it, the explanation of regression to the mean is more than worth the price of the book. After reading it on loan from my local library and even before returning it, I realized I would want to refer to it again and again and purchased it.I studied basic statistics and statistics for social sciences in college many years ago. I wish this book had been available then. While my grades would have been better for it, more than that, my understanding would have certainly been enhanced.

Gary Smith's book Standard Deviations is an enjoyable, highly readable overview of using statistical analysis correctly and the pitfalls that can accompany its abuse. Unlike seemingly every other reviewer here, I am neither a PhD nor a statistician, but I do not believe this particular book is targeted to such a narrow audience. Statistics is a topic that many people find as exciting as watching paint dry, but Smith's lively writing style and plentiful examples help keep the reader's interest regardless of their underlying interest or expertise in the field. Concepts are clearly explained, demonstrated with an example or two, and summarized again. Occasionally, I found some of the content to be of limited value. The chapter on graphs was not improved by the discussion of chartjunk, in my opinion. Nevertheless, I enjoyed the book overall and will likely read it periodically as a reminder to think critically about any 'facts' proven by this or that statistical analysis.

I am no expert on stats and I never attained a PHD but that didn't stop me from enjoying this book tremendously. If you are like I was in college and hated your stats class don't that deter you from purchasing this book! Professor Smith's insights are conveyed in an easy to understand manner and his instructions are very valuable to everyone! You will no longer accept academic studies on face value and be skeptical the next time you read in the newspaper about a study that doesn't pass the common sense test. After reading this book you will be armed with strategies to avoid being duped by people who are data farming. Everyone should read this book at least twice!

This makes statistics fun & you learn so much, especially how you are being fooled everyday. I bought 3 more copies & sent them to friends, some of them MBA's & big-time stock buyers & they too have learned a lot.

Like the previous reviewer, I have a Ph.D. in a related field (finance) and also like him, I had never heard of the author, Dr. Smith, before seeing this book last week. After that we are alike in no apparent way. I heard no NPR interview, but when I saw this book on the shelf, I flipped through it and liked its contents well enough to buy it. I did not research the author's CV to decide whether his logic is sound; when a publication is in my area of expertise, I prefer to make that evaluation myself. I am only about a third of the way through the book, but so far I sufficiently impressed that I will probably make it assigned reading the next time I teach a doctoral class that focuses on methodological issues. If you're not doctoral student, don't worry; the book is written for a reasonably bright layman, but many of the issues Dr. Smith raises are common (but not obvious) errors I have seen many researchers make, and I really like seeing so many of them summarized in one place.Indeed, I find it difficult to understand the frame of reference from which the previous reviewer sees the book. He starts by saying "most of the stuff Smith covers has been extensively documented elsewhere," but then goes on to imply Smith is unqualified to summarize said documentation. He criticizes Dr. Smith because "it [sic] like he enjoys taking pot shots at other more respected economists," but then himself asserts that "[Dr. Smith's] CV implies that he was denied tenure at Yale," and then refers to his "bitterness" and his "being willfully blind to the real world." "Enjoys taking pot shots?" Physician, heal thyself. Let me address just the implications of the conjecture that Dr. Smith was denied tenure at Yale. Yale is a world-class university, and Dr. Smith was undoubtedly selected from a set of a hundred or more applicants from other top universities. The fact that Dr. Smith was selected from that pool strongly signals to me that he is exceptionally well trained and almost certainly sufficiently well-qualified to write such a book.I do thank the previous reviewer for pointing me in the direction of "Murmane [sic] and Willett's" book, which he suggests summarizes "plenty of rigorous statistical methods for making valid inferences from observational data." This is a big issue in finance and economics [e.g., unlike a chemist who can control temperature or a physicist who can control velocity, a finance professor cannot tell Apple to fire its CFO so we can see what the effect on share price is], and the most common way of trying to deal with it is with instrumental variables (IVs). However, while this approach is theoretically quite sound when the right IV is available, in practice it is typically difficult to find an appropriate IV. My own personal suspicion is that using an imperfect IV creates at least as many problems as it solves (indeed this is a research area I intend to pursue myself when time permits), but I look forward to reading Murnane and Willett's analysis of this issue.OK, enough about the previous review, what about the book itself? The author is quite correct that, given the pressure to publish, many researchers will be inclined to look at different combinations to try to find statistically significant relations between variables of interest, and that this changes the statistical significance level when such a relation is found. We all see the results of this regularly. Last year eggs were bad for your health; this year they are good. Last month vitamin pills were good; this month they are bad. The traditional wisdom is that because protein is hard on the kidneys, feline renal problems should be treated with a low-protein diet, but there is a growing body of research (e.g., see Hodgkins' book Your Cat, a real bargain at the Kindle price of $8.89) suggesting that in the wild, a cat's diet is about 2/3 fat and 1/3 protein, and when we give them the low-protein diet characteristic of dry food we are actually causing renal (and other) problems, not solving them. Is coffee good or bad? It depends on whether you are reading today's newspaper, or yesterday's, or last week's. The list of such flip-flops is endless, and indeed the medical journals seem to be taking the lead in addressing this problem (e.g., Ioannidis' Why Most Published Findings are False, http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.0020124).Why are all these tests so inconsistent? Part of the reason is that many are based on observational data and, the IV method notwithstanding, it is difficult to determine cause and effect from observational data. Part of the reason is that sometimes researchers will keep looking until they find a statistically significant result that supports their hypothesis, and then use the same data to "confirm" that hypothesis. Sometimes there was a bias in the way the samples were selected. Sometimes the sample size is too small, or the data were grouped incorrectly, or the wrong data were excluded. If you want a better understanding of these issues, and to be better prepared to assess for yourself what you see in the media, I highly recommend this book.

This was a fun read. Smith finds the delicate balance between educational examples and genuinely fun anecdotes. I recommend this book to those who wish to understand the perils of flawed statistical analyses. It helps to have some statistics background, but it is not required.

This would make a great supplemental book for science or math class for a high school student. It presents an opportunity to clear up misconceptions early and encourages healthy skepticism. I especially liked the discussions about randomness.

A lot of great examples of how data gets used incorrectly. Nothing super advanced, but really valuable for someone starting off in Statistics

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