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Statistics Done Wrong: The Woefully Complete Guide

4.15  ·  Rating details ·  802 ratings  ·  94 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
Jordan Peacock
Mar 13, 2015 rated it it was amazing
God, this was depressing. Bitter pill, but better to swallow it now.
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
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
Hendry Nicholas
Aug 28, 2020 rated it it was amazing
I am looking for potential statistics homework help for my university exams. Please let me know if I can hire one of your expert tutors and what is the cost of it. I have heard from many clients that your company is exceptional in providing statistics assignment help also. So, I am waiting. If one your tutors can help me, I will take regular classes since I want to score good grades during my exams. In case you aren’t able to assign a tutor recommend someone who can assist. ...more
Jan 17, 2020 rated it really liked it
Shelves: 2020, non-fiction
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...
Jan 17, 2020 rated it really liked it
A very brief summary of bloopers in statistics.
Mar 15, 2018 rated it really liked it
I should have taken more math in college. Great book.
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.
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
Mar 23, 2015 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
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
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.
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).
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.
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.
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
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
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
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
Deane Barker
Mar 18, 2019 rated it it was amazing
You can read this book on three levels --

1. Statistics is easy to screw up.
2. Here are all the ways you can screw them up.
3. Here is the actual math that proves it's being screwed up.

#1 is absolute -- you cannot come away from this book without knowing that statistics is a messy science. I got about half of #2 -- I have a better idea of how statistics can be wrong. #3 is rough -- especially in the beginning, there's some math that was just lost on it.

But this is still a great book, because socie
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
Dana Kraft
Oct 13, 2016 rated it really liked it
Shelves: nonfiction, business
This book is written for scientists, which I'm not. However, I found it really interesting and applicable to what I've done with statistics and data analysis in the marketing world. Software is making it a lot easier for a marketer to become an armchair statistician, and there are dangers lurking in that space. It's really easy to get cynical about all data analysis after reading this. To me, it reiterated that data analysis can not stand alone outside of business sense and subject matter expert ...more
Mar 04, 2018 rated it it was amazing
Primarily aimed at scientists, but also highly relevant to anyone who works with data. There aren’t many equations or formulae, rather it goes into greater depth on the common statistical mistakes than most of the other books on this list.

In its own words, ‘it explains how to think about p values, significance, insignificance, confidence intervals, and regression’.

By the time you’ve finished, you’ll be able to spot a dodgy A/B test from a mile off! Since it’s geared towards a more academic audie
Feb 28, 2017 rated it it was amazing
This book is very well-written, engaging, easy to read, and informative. It is a non-technical exposition of the many ways in which a researcher can fail and mislead due to incompetent or mindless use of statistical methods. Examples are chosen well (most of them are at least mildly amusing) and come from various fields, mostly medicine and psychology. Portions of the book present material that is very basic but hey, I don't think we as a community of researchers deserve to discard basic informa ...more
Mar 20, 2017 rated it it was amazing
Shelves: science, engineering
As an engineer who has read thousands of scientific articles for research purposes, this book is life changing. Seriously, it's as if my entire worldview is shaken! This book details many of the typical statistical errors pervading science and engineering research.

Reinhart provides compelling evidence that a large portion of scientific research findings (or interpretations thereof) are probably bogus to some extent or another. In addition to providing numerous examples of the types of common st
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