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Statistics Done Wrong: The Woefully Complete Guide
by
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 scientistsyes, even those published in peerreviewed journalsare doing statistics wrong.
"Statistics Done Wrong" comes to the ...more
"Statistics Done Wrong" comes to the ...more
Paperback, 176 pages
Published
March 16th 2015
by No Starch Press
(first published April 27th 2013)
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(showing 130)
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, overreliance on pvalues, etc. (similar to Motulsky Reinhart prefers 95% Confidence Intervals). The second half focuses more on reproducibility, statistical fishing etc.
It's a very wellwritt ...more
It's a very wellwritt ...more
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 posthoc 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 ...more
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 ...more
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 ...more
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 ...more
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
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Užitečná kniha upozorňující na nešvary v designu dnešních výzkumů  dezinterpretace p hodnot, pseudoreplikace, nedostatečná statistická síla, publication bias... Autor je vtipný, aktuální a přidává řadu praktických tipů (např. na datová úložiště nebo stránky, kde je možné provést preregistraci vašeho výzkumu).
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 ...more
I consider myself reaso ...more
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 nonfiction it has added several entries to my toread 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
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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 ...more
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 ...more
This is your goto 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.
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).
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 pvalues because, in addition to the problems with pvalues themselves, requiring stricter tolerances often means that while the result measured i
...more
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
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 ...more
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 ...more
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 ...more
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 ...more
This book is very wellwritten, engaging, easy to read, and informative. It is a nontechnical 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
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"Truth inflation arises because small, underpowered studies have widely varying results. Occasionally you're bound to get lucky and have a statistically significant but wildly overestimated result." (26)
"A major objection to dichotomization is that it throws away information. Instead of using a precise number of every patient or observation, you split the observations into groups and throw away the numbers. ... In general, this loss of power and precision is the same you'd get by throwing away ...more
"A major objection to dichotomization is that it throws away information. Instead of using a precise number of every patient or observation, you split the observations into groups and throw away the numbers. ... In general, this loss of power and precision is the same you'd get by throwing away ...more
This book is an introduction to some common mistakes in statistical analysis. I liked its breadth but not its depth. I was already aware of most of these issues, but my understanding of them is pretty shallow. As a result, at times I felt like the book was moving too quickly  I wished we could've stopped a delved a little deeper into some of the topics. There were also a lot of examples drawn from published papers  which is good to help me see how "real" the problem is  but it would've been n
...more
A guide for people, especially academics, who had statistics training that, like most, focused on statistical theory or rote calculations rather than how you actually correctly apply anything you're learning. Anyone who is even vaguely aware of the "replication crisis" will not find anything much new here, and Reinhart's suggestions are the same as everyone else's (preregister experimental protocols, do power analysis and formulate stopping rules in advance, use confidence intervals rather than
...more
Statistic Done Wrong is a great read, that highlights the different mistakes that are commonly made by researchers with a weak statistical background. But these are not only mistakes done by researchers from other fields, but also by professional statisticians themselves. Alex Reinhart decides to avoid mathematical equations in this guide and successfully uses text and a few graphics to introduce the reader to the basic statistical concepts necessary to understand the mistakes described in each
...more
I may not have learned how to use stats from this book, but it sure gave some great stories about how not to do it. So much of successful data science, and, well, science is that sniff test for when stats feel manipulated, and this book gave a language for the problems I already knew by smell.
A quick read, and a good guide for us amateur statisticians out there.
A quick read, and a good guide for us amateur statisticians out there.
A great introduction into common pitfalls in statistics. I'd say its a good book aimed at people who've learned the basics of statistics from an undergraduate level and want to use it in a research paper or reviewing a research paper. If you're looking for a mathy style book, its best look elsewhere but this book will tell you WHAT to look for
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“Misconceptions are like cockroaches: you have no idea where they came from, but they’re everywhere—often where you don’t expect them—and they’re impervious to nuclear weapons.”
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“The first principle is that you must not fool yourself, and you are the easiest person to fool. — Richard P. Feynman”
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