Statistics Done Wrong: The Woefully Complete Guide 1st Edition
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"Of all the books that tackle these issues, Reinhart's is the most succinct, accessible and accurate." -- Tom Siegfried, Science News
"A spotter's guide to arrant nonsense cloaked in mathematical respectability." -- Gord Doctorow, BoingBoing
From the Author
Statistical analysis is tricky to get right, even for the best and brightest. You'd be surprised how many pitfalls there are, and how many published papers succumb to them. Here's a sample:
- Statistical power. Many researchers use sample sizes that are too small to detect any noteworthy effects and, failing to detect them, declare they must not exist. Even medical trials often don't have the sample size needed to detect a 50% difference in symptoms. And right turns at red lights are legal only because safety trials had inadequate sample sizes.
- Truth inflation. If your sample size is too small, the only way you'll get a statistically significant result is if you get lucky and overestimate the effect you're looking for. Ever wonder why exciting new wonder drugs never work as well as first promised? Truth inflation.
- The base rate fallacy. If you're screening for a rare event, there are many more opportunities for false positives than false negatives, and so most of your positive results will be false positives. That's important for cancer screening and medical tests, but it's also why surveys on the use of guns for self-defense produce exaggerated results.
- Stopping rules. Why not start with a smaller sample size and increase it as necessary? This is quite common but, unless you're careful, it vastly increases the chances of exaggeration and false positives. Medical trials that stop early exaggerate their results by 30% on average.
- Item Weight : 11.2 ounces
- Paperback : 176 pages
- ISBN-10 : 1593276206
- ISBN-13 : 978-1593276201
- Product Dimensions : 6.13 x 0.46 x 9 inches
- Publisher : No Starch Press; 1st Edition (March 1, 2015)
- Language: : English
- Best Sellers Rank: #61,519 in Books (See Top 100 in Books)
- Customer Reviews:
Top reviews from the United States
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Just make sure you are not new to statistics. If you start with this book, you will learn nothing useful, only some tidbits here and there, mostly unconnected.
This book will tell you only about p values and power of a test. Some 90% of the contents of this book are related to what people does wrong with regards to p values, which is a lot, I have to say.
I had no idea of the current status of many science topics and this book sadly illustrates about it. You will learn that many doctors, scientists and even reviewers have no idea about what they are talking about or commenting on.
You need to be confident with the use and understanding of many statistics before you get to read and understand this book. This is not a book for everyone, this is for sure. Neither is it a book about statistics. It is a book on the misuse and bad implementation of p values and how people dealing with statistics make the wrong question and get the wrong answer out of their statistics and their data sets.
If you are fluent with p values and the power of the test, and you can deal with hypothesis testing and all that stuff, then go read this book and you will learn something really useful.
If you are not used to statistics, this book will teach you nothing. But it is a very well written book, a nice piece of any collection. So go get it even if you cannot deal with statistics. Then, learn statistics because it is a huge investment anyway. When you are done, read this book and learn something else.
Statistics, however, was a favorite class of mine.
A few "similar" books come to mind, including (a) the drier "Common errors in statistics" by Phillip Good, (b) the three terrific popular books by Ben Goldacre - "Bad science", "Bad pharma" and "I think you'll find it's a bit more complicated than that" - and (c) the elegant "Understanding the new statistics" by Geoff Cumming. (I have not seen "How to lie with statistics" by Huff and Geis). Reinhart's book is more "big-picture" than Good's, and broader than Goldacre's or Cumming's. (The latter is a perfect "single-issue" book; the former are not specifically about cataloging statistics errors).
Statistical semi-literacy of empirical researchers is a serious problem, and any effort to improve the situation is to be lauded. Alex Reinhart's book - engagingly written, and nicely produced (and fairly cheaply sold) by No Starch Press - is a force for good, and one which can have a material impact.
Top reviews from other countries
The book is a nice extension of the online one (which is available for free). The author is very clear and provides many examples to explain simple, but a bit confusing, concepts. Such as the p-value.
Applying statistics correctly is hard, even if you do understand the theory. This has affected a lot of research negatively. Many studies are not reproducible because they use flawed statistics, which erodes our scientific foundation over time.