Ebook Statistics Done Wrong: The Woefully Complete Guide
Ebook Statistics Done Wrong: The Woefully Complete Guide
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Statistics Done Wrong: The Woefully Complete Guide
Ebook Statistics Done Wrong: The Woefully Complete Guide
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Review
"If you analyze data with any regularity but aren't sure if you're doing it correctly, get this book." -- Nathan Yau, FlowingData"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
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From the Author
What goes wrong most often in scientific research and data science? Statistics.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.
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Product details
Paperback: 176 pages
Publisher: No Starch Press; 1 edition (March 1, 2015)
Language: English
ISBN-10: 1593276206
ISBN-13: 978-1593276201
Product Dimensions:
6.1 x 0.5 x 8.9 inches
Shipping Weight: 10.4 ounces (View shipping rates and policies)
Average Customer Review:
4.4 out of 5 stars
90 customer reviews
Amazon Best Sellers Rank:
#112,470 in Books (See Top 100 in Books)
Let me front-load the criticism. I wish an experienced statistics instructor had reviewed the manuscript. The book does better in its second half, where it discusses what I would call problems with empirical-research culture, than in its first half, which has more textbook statistics. The author neglects to explain the basics - things like "sample", "statistic", "sampling distribution", "conditional probability" - and often confuses matters by bringing in issue Y when setting out to discuss issue X. (Appropriately, a section named "Confounding Confounders" is itself confounded: we start talking about "coarsening" data (not what I expected based on the title, by the way; a Y-for-X switch already took place), then get into something else. I will single out the introduction to the "base-rate fallacy" as another weak spot). A choice to be non-technical means that solutions to some problems cannot be effectively presented - although sometimes they are suggested after all. The "woefully complete" part of the title is, I take it, tongue-in-cheek, so no quibbles there.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.
This book is great!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.
A fantastically interesting book. Anyone who loves numbers, and loves science will appreciate the compiled research and examples Alex Reinhart published here. Many times, reading the stories and statistical studies, I thought back to my Ivy League university experience. Often times, a professor quoted some study based on a particular topic, described the nature of the study, and then presented the conclusion. Too many times, the conclusion struck me as nonsensical or counterintuitive. I asked several professors, "Why did they conclude "A" and not "B" or "C" or even something else? None knew answers to these questions. Then, I asked them, "Where is the data? Have you ever seen the data?" No, they had not. One tenured professor shrugged and told me, "You can try writing to the experimenter and asking to see it." That blew me away. Professors at top universities believed and professed unproven conclusions they had personally never challenged.Statistics, however, was a favorite class of mine.
This book only has one equation as far as I remember. It doesn't explain how to do statistics, and doesn't explain the math of how statistical tests work, or the proofs that they work. Instead it explains what the results mean, and the many many ways statistical results can mislead. It covers clearly and humorously topics missing from almost all statistics texts (but not mine!): pseudoreplication, multiple comparisons, false discovery rate, publication bias, Simpson's paradox, the challenge of nonreproducibility of published findings, .... Anyone evaluating published research, or anyone analyzing data, must understand these topics to understand (or do) science properly. This book is highly recommended.
This is an exceptional gazette to the current poor state of statistically-based research. Reinhart is efficient in his enumeration of the major sins committed by researchers, usually unfamiliar with the nuances of the statistical concepts that are made easily available to them in stats packages. Furthermore, he provides many useful tips for how to countering much of the backsliding taking place in research.Complex topics were made easy to understand, and best of all, not a word in the text was wasted. I read this in two afternoons and am better for it.
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