Statistics Done Wrong: The Woefully Complete Guide (2020)
Statistics Done Wrong The Woefully Complete Guide 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 co
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 theEveryone 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 rescue with cautionary tales of all too common statistical fallacies It ll help you see where and why researchers often go wrong and teach you the best practices for avoiding their mistakes.In this book, you ll learn Why statistically significant doesn t necessarily imply practical significance Ideas behind hypothesis testing and regression analysis, and common misinterpretations of those ideas How and how not to ask questions, design experiments, and work with data Why many studies have too little data to detect what they re looking for and, surprisingly, why this means published results are often overestimates Why false positives are much common than significant at the 5% level would suggestBy walking through colorful examples of statistics gone awry, the book offers approachable lessons on proper methodology, and each chapter ends with pro tips for practicing scientists and statisticians No matter what your level of experience, Statistics Done Wrong will teach you how to be a better analyst, data scientist, or researcher.

[PDF] ✓ Free Read ☆ Statistics Done Wrong: The Woefully Complete Guide : by Alex Reinhart ✓ 151 Alex Reinhart

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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 on reproducibility, statistical fishing etc.It s a very well written short [...]
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 into the ethics of research and research publication It is interesting but not really new especial [...]
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 q [...]
U ite n kniha upozor uj c na ne vary v designu dne n ch v zkum dezinterpretace p hodnot, pseudoreplikace, nedostate n statistick s la, publication bias Autor je vtipn , aktu ln a p id v adu praktick ch tip nap na datov lo i t nebo str nky, kde je mo n prov st preregistraci va eho v zkumu.
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 fallacies [...]
God, this was depressing Bitter pill, but better to swallow it now.
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 avoiding t [...]
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 backgro [...]
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 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.
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.
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.
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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 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 is lik [...]
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 expertise. [...]
A quick read and entertaining I think I learned some things although I honestly think I m 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 than twice, but those must be the most famous examples, because that was weird I consider myself reasonably statistica [...]
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 academic audience, it [...]
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 statisti [...]
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 information [...]
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 over estimated 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 a third of y [...]
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 nice if there [...]
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 p st [...]
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 ch [...]
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 math y style book, its best look elsewhere but this book will tell you WHAT to look for
Enjoyed the selected anecdotes on how statistical analyses have been applied incorrectly in research papers It would have been better if the author could have included figures from papers he mentioned to better illustrate the key points he was trying to make.
Never finished Got to chapter 6 Book is interesting and I plan to finish eventually It is just a little dense for everyday reading.
An interesting read even for stats practitioners, it happens that we don t think enough to experiment design and results Reinhart is able to spot these issues in non technical terms and is therefore accessible to everyone
After reading Alex Reinhart s overview of how researchers routinely misuse statistics, you will never trust a newspaper account of breakthrough research again Not without asking questions about how the research was conducted That s the point The root cause of many of these ills is simple ignorance of key statistical principles and poor research design Yet there plenty of incentives for researchers pursuing an academic careers, pharma companies pursuing profits and journals pursuing relevance and [...]
tl dr Confidence intervals and Statistical power Use them.A great survey of the ways scientists lie to ourselves on the road to publication Lies that are mostly unintentional Each chapter covers a basic statistical method by discussing what goes wrong when you aren t rigorous in your approach and fall into one of a dozen different traps.This isn t a stats book in the typical sense, which is welcome By keeping the focus on description, you come away with a better sense of why you need to be warry [...]
For someone working with data but who doesn t have a background in statistics, I always feel that I need to upgrade revise in terms of my statistics knowledge This book seems quite promising However, there are certain bits that made me feel my statistics knowledge wasn t good enough to understand some of the stuff it was saying some of the points were covered too briefly, and some of the points were not made very clear, to me at least Overall, I think this book is quite a nice read, especially t [...]