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Statistics Done Wrong: The Woefully Complete Guide

4.18  ·  Rating details ·  980 ratings  ·  118 reviews
Everyone knows that abuse of statistics is rampant in popular media. Politicians and marketers present shoddy evidence for dubious claims all the time. But smart people make mistakes too, and when it comes to statistics, plenty of otherwise great scientists--yes, even those published in peer-reviewed journals--are doing statistics wrong.

"Statistics Done Wrong" comes to the
Paperback, 176 pages
Published March 16th 2015 by No Starch Press (first published April 27th 2013)
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Always Pouting
Jun 05, 2019 rated it it was amazing
Trying to ease myself back into statistics and this felt like a good way to do so. Especially felt relevant since it centers the conversation around scientific publishing and the misuse of statistics in a lot of research papers. I actually found out about some places where scientists can publish there data sets and I hadn't know about that and now I'm super excited to take a look and see if there's anything I can play around with there. The material covered is pretty accessible and it's a good r ...more
May 13, 2015 rated it really liked it
Shelves: mathematics
You could say this is a mix of Motulsky's Intuitive Biostatistics and Goldacre's essays. The first half of Statistics Done Wrong are plain English essays on various problems encountered in modern science related to statistics, problems which crop up again and again, such as the multiple comparison problem, over-reliance on p-values, etc. (similar to Motulsky Reinhart prefers 95% Confidence Intervals). The second half focuses more on reproducibility, statistical fishing etc.

It's a very well-writt
May 27, 2015 rated it really liked it
Shelves: data-science
If you're used to statistical analysis, you won't much that is new here: pay attention to statistical power, beware of multiple comparisons and repeated measurements without post-hoc tests and measure of effect size. However, the book is a good series of cautionary tales for new students in statistics and research methods. It is highly readable.
Towards the end, the book veers a bit off course and get more into the ethics of research and research publication. It is interesting (but not really ne
Mar 16, 2015 rated it really liked it  ·  review of another edition
Shelves: math
Let me preface this review by saying that if you're looking for a book to learn statistics from, this is not it. The author assumes a certain knowledge on the subject matter and unless you have that, you probably won't get much out of this text as explanations are a bit on the terse side (though heavily referenced for additional reading).

So who is this book for then? Everyone who works with statistics and/or data analytics, and wants to get a handle on some of the most common mistakes and fallac
Jan 22, 2018 rated it it was amazing
I'm a sociologist who's taken several statistics course in both undergrad and grad school, have worked at a research center, and have taught research methods at the undergraduate level. I tell you all that because you need to understand statistics is one of my particular flavors of nerd. I find it infinitely frustrating when a student will find a peer reviewed, scientific article on which to base their position only to dismiss the multiple peer reviewed, scientific articles published later which ...more
Bastian Greshake Tzovaras
May 14, 2015 rated it it was amazing
If you haven't had a good introduction into statistics: This might just be what you're looking for. Explains all the honest mistakes (and evil hacks) you can make while analysing data. If you're already familiar with stats it still might be a nice book to refresh your knowledge (and laugh a lot, because it's written very well). ...more
Nathan Albright
Jun 15, 2021 rated it really liked it
Shelves: challenge-2021
The problem with this book, such as it is, is that it by no means a woefully complete guide. To be sure, the state of statistics abuse in contemporary society is rather woeful, and this book demonstrates that a great many people, including those who engage in data analysis as a profession, lack a fundamental understanding of the terminology and meaning of the field they work in, have a terrible understanding of statistics. The author, though, does not go the direction that one would expect, and ...more
William Schram
Mar 09, 2019 rated it it was amazing
The intent of Statistics Done Wrong by Alex Reinhart is to foster a good statistical method from scientists and laymen. Mr. Reinhart doesn’t demonstrate how to calculate these items himself, but he does show how to avoid both the most egregious errors and the most subtle mistakes that people perform when using statistics to prove things. This is an important task since Statistics has a bad rap for aiding people in lies. However, Statistics is a tool and not a magic bullet. There are multiple way ...more
Feb 19, 2018 rated it really liked it
Shelves: non-fiction, 2018
A quick read and entertaining. I think I learned some things (although I honestly think I'm more confused about some things after this). Worthwhile for the curious, I guess, although I am not sure how it compares to other books on the topic. (I will say the examples of Simpson's Paradox were the exact same two used in a video I watched about that topic recently. I assume this has happened more than twice, but those must be the most famous examples, because that was weird.)

I consider myself reaso
Oct 25, 2016 rated it really liked it
I liked this. It's a short, straightforward, and clear look at a variety of bad statistical practices. It won't tell you how to do a regression or a hypothesis test but it will discuss which to use. The narrative is clear and straightforward, and readily readable to anybody with a moderate mathematical or technical background.

It's mostly stuff I think I already knew, but it was helpful to have it systematically and clearly presented.

The author is a CMU statistics grad student with a physics back
Jan 31, 2019 rated it really liked it
This was a decent short read about poor practices in conducting research and reporting results, especially in the medical & neuroscience domains. Some of the examples cited were especially troubling:
- "If you administer questions like this one [a typical question about base rate fallacy] to statistics students and scientific methodology instructors, more than a third fail. If you ask doctors, two thirds fail." Yikes
- "of the top-cited research articles in medicine, a quarter have gone untested a
Sep 05, 2020 rated it really liked it
Clearly elucidates the basic concepts for:
1. Normal distribution
2. How to use p value with confidence intervals?
3. When to use p value?
4. How to tread between the fine line of using deceptive statistics vs reading the actual impact

A lot of strategies in the organisation is built seeing the co-related data but there is never an attempt to find the causation. This book highlights how we can do so with the examples in Pharma R&D industry.

Overall, it is a good read and a highly recommendable one.
Apr 25, 2015 rated it really liked it
Fun, quick read covering much the same territory as The Cult of Statistical Significance. Well-written and not totally pessimistic about the state of scientific analysis today, despite many examples of fairly severe ineptitude.
Jan 17, 2020 rated it really liked it
Shelves: non-fiction, 2020
If you have trust issues, don't read the book. It takes don't trust statistics that you haven't falsified yourself to a whole new level. How can we trust doctor's when their knowledge is based on false statistics? Better not think too much about it... ...more
Jordan Peacock
Mar 13, 2015 rated it it was amazing
God, this was depressing. Bitter pill, but better to swallow it now.
May 03, 2015 rated it really liked it
It was good.

Very much in the same vein as How Not To Play Chess by Znofsko-Borofsky.

Also very much aimed at the biostatistics realm, but applicable to everyone who does data work.
Mar 15, 2018 rated it really liked it
I should have taken more math in college. Great book.
Jan 23, 2022 rated it really liked it
Shelves: reviewed, science
Reinhart's book on statistical mischief (deliberate or not) aims to create awareness and understanding of statistical fallacies and abuses without going into the mathematical definitions or details of how the statistical methods investigated in this book work. Instead, when he covers several methods on how to find determine statistical significance, he describes them by their effects, their strengths and weaknesses and in what situations they can be (ab)used. The target audience are scientists o ...more
Degenerate Chemist
Jan 26, 2022 rated it it was amazing
Shelves: qe-stuff
This book is an excellent little collection of essays on the misuse of statistics in scientific disciplines. It is the first book I have ever read that has been able to articulate my frustrations with p-values. I do think that it brings up one big important point- that so few scientists have the statistical knowledge necessary to process the mountains of data they collect. I didn't really have any statistical background until I trained as a QE.

The author states in his introduction that he wants
Shelly (YI-Hsuan) LIN
Dec 13, 2020 rated it really liked it
Shelves: statistics
Good overview of common mistakes scientific research papers did with misleading statistics results. Good outlines of things we should be careful about while conducting our own research. Good and concise book to read.
Jan 17, 2020 rated it really liked it
A very brief summary of bloopers in statistics.
May 11, 2019 rated it liked it
Shelves: academic-stuff
This is not a "complete" anything. It's a few chapters walking through a few basic concepts of statistics that people often ignore, misunderstand or don't even know about. If you're a psychology graduate, this will all be old news to you, although a refresher in statistics never hurts. If, on the other hand, you have to deal with statistics quite often but aren't really on solid grounds, a book like this is a pretty good idea.

The main value of this book I think are all the anecdotes of statistic
Feb 08, 2019 rated it it was amazing
A brief, lovely, vaguely horrifying overview of how endemic "bad statistics" is. This is mostly pitched to the statistics practitioner - and especially one coming from academia. In other words, this would've been catnip to me like ~5 years ago. But, for now, having already cleansed myself in the work of Data Colada, Gelman and Ioannidis, much of this was old hat.

Yes, people over-rely on and misinterpret p-values. Yes, people "double-dip" and torture/exhaust their data, hunt for statistically sig
Andrew Chen
Feb 23, 2020 rated it it was amazing
great set of examples of common statistical mistakes that can be unintuitive. lots of examples of existing literature that screw some of these up. not gonna lie, makes me rather wary of pretty much all medical research. some key points to remember:

ch2: confidence intervals offer more information than p-values and can be used to compare groups of different sizes. statistical power is very important, and underpowered studies might result in truth inflation. statistically insignificant does not mea
Christina Jain
Dec 06, 2015 rated it it was amazing
This is your go-to book if you need a breakneck primer on statistics. It only takes a few hours to read and at the end of it you'll be familiar with confidence intervals, standard error, power, catching multiple comparisons, truth inflation, and more! The goal of the book isn't to teach you how to do the calculations but rather to give you a basic understanding of the things statisticians concern themselves with and common misconceptions to watch out for. ...more
Nishant Pappireddi
Jul 24, 2015 rated it it was amazing
This was a really interesting book that talked about a lot of common statistical errors in scientific research. There were a lot of funny examples, including an MRI of a dead salmon. However, this book has made me more pessimistic about my ability to do statistically rigorous research.
T. Ellis
Oct 09, 2021 rated it it was amazing
Shelves: owned, read2021
A new favourite. We all* know p-values are a load of hogwash, but Reinhart's book doesn't just state that so we can all chortle to ourselves and complain about all their misuses; no, his book succinctly details the philosophical history of how the very concept of 'statistical significance' developed, and how to pick the best alternative for any situation -- much of which I had never known or at least absorbed.**

I wish it were much longer and in depth. (As it is, it focuses far too much on statis
Erika RS
Nov 04, 2017 rated it really liked it
This book was a great dive into some of the gotchas that make statistical analysis of data challenging. If I were to try to narrow the common analysis mistakes to one theme, I would say that the common thread of much bad statistical analysis is trying to get more information out of the data than it can really yield. The answer isn't just to lower your p-values because, in addition to the problems with p-values themselves, requiring stricter tolerances often means that while the result measured i ...more
Fraser Kinnear
Mar 31, 2019 rated it really liked it
Shelves: science, math
I loved this. It’s a fast read, in a conversational tone and level of detail, describing various problems that come up when trying to apply statistical analysis. It reads like a long conversation one might have over drinks with a scientist friend.

Topics include: type M error and the problem of underpowered statistics, pseudoreplication, the base rate fallacy, the problems with p values and why confidence intervals are so much better, double dipping data and when to stop a study, the problems wit
Oct 19, 2021 rated it it was amazing
Reinhart gives a highly readable and surprisingly fun roundup of common errors in statistical analysis in the spirit of books like Innumeracy and How To Lie With Statistics. Although this account differs from those particularly in its focus and thorough documentation (like most great non-fiction it has added several entries to my to-read list). The focus here seems to be of the "for the working scientist" sort in both its selections of errors as demonstrations and in its practical means for avoi ...more
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