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 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 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 more common than "significant at the 5% level" would suggest By 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.

# 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 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 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 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 more common than "significant at the 5% level" would suggest By 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.

Compare

4out of 5Always Pouting–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 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 read for people who read or do research to get a better understanding of what to look out for/what better usage of statistics would look like. Not really a good choice to pick up if you're just trying to learn statistics which the book lets you know right away. It does include resources though for people trying to learn statistics and get more comfortable with, some of which I hope to get to at some point.

5out of 5Philipp–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 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-written short overview of the most egregious errors in science, so I think it's a good fit for working scientists interested in improving their statistical analyses. It won't make you a statistician, for that it's too short.

4out of 5SocProf–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 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 new... especially in light of the whole recent Lacour fiasco) but it does not necessarily have to do with statistics done wrong. Nevertheless, if you teach intro to statistics, the book is a good additional reading as it is not so much about computation, and more about statistical reasoning and understanding the strengths and weaknesses of different tests.

5out of 5Michael–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 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 committed in the field, whether knowingly or unknowingly. Like mentioned before the style can be a bit terse, and I think occasionally chapters could have benefitted from slightly more background on the presented concepts, especially since this book is marketed as a "complete guide". I nonetheless consider it a good resource for people as myself, who mainly picked up their statistical knowledge in relation to their main interest, i.e. for machine learning or bioinformatics. If you feel like you have at decent handle on basic statistics, but wouldn't trust yourself to set up your own analysis or experiments, you'll certainly gain something from "Statistics Done Wrong". On a stylistic note, I have to say that for a book on statistics, this has been a surprisingly entertaining read and the author deserves some bonus points for pointing out the irony of using published studies and papers to point out fallacies in other studies and papers. If you are an experienced statistician you probably can give this one a pass, but if you want to freshen up or add to your existing basic statistics knowledge, this is a very enjoyable book.

4out of 5Gina–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 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 question the earlier research findings. It is for this reason, this quote particularly caught my attention: "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." Despite the cartoonish cover, Reinhart seeks to seriously critically examine much of the published statistical analysis, particularly in the medical field among others, in several key areas: lack of education in statistics leading to misinterpretation of findings, bias - both intentional and unintentional, statistically significant findings v. practically significant findings, fraud, data set errors. There is much math within, but I found it well explained and approachable for even those with no prior statistics knowledge. I thought the discussions of regression analysis, p values, and confidence intervals were especially well done, and the examples he uses are interesting. I highly recommend this as a beginner's guide to anyone considering statistical research in any field.

5out of 5Jordan Peacock–God, this was depressing. Bitter pill, but better to swallow it now.

5out of 5William Schram–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 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 ways to run equations and regressions on data, so choosing the right operation to perform is imperative. The book is written in a style that doesn’t expect much prior knowledge in Statistics. This is fortunate since Statistics is complicated and has a ton of terms for various things. There are p-values and chi-square regressions and all sorts of analyses that one can do to data. The book is split into 11 chapters with each chapter discussing something that can go wrong in the wonderful world of Statistics. It utilizes examples from real scientific studies. The book does mention the most important aspects of Statistics, but it is not filled to the brim with formulae; this is the main reason that the layman can understand this book. There is one unusual part about this book though, it is shorter than I thought it would be. While the book looks like it should be around 20-30 pages longer than it is, it is only 128 pages of content. This is because the book uses really sturdy paper. Other than that small detail this book is really well done. It is informative and has a bit of a sense of humor to it.

5out of 5Trang–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 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 after their publication, and a third have been found to be exaggerated or wrong by later research." Double Yikes There were some parts of the book that would probably be unclear without basic stats background (most notably the explanations in Multiple Comparisons), while some other basic concepts were explained in a somewhat lengthy way (e.g. standard deviation -- I would have preferred a concise equation). This book is available online: https://www.statisticsdonewrong.com/i...

5out of 5Hendry Nicholas–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. 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.

4out of 5Julia–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...

4out of 5Raghoonandh–A very brief summary of bloopers in statistics.

5out of 5Michael–I should have taken more math in college. Great book.

5out of 5Manu–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. 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.

4out of 5Unwisely–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 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 reasonably statistically sophisticated, but he still managed to come up with a couple of new concepts for me (statistical power, for example, which seems like it should be something I've heard of before) as well as mistakes I didn't know you could make. (Luckily I don't think I'm making any of them.) At the end it gives a link for "updates, errata, and other information". I looked, and to save you the trouble, there are only two minor corrections. Also some glowing reviews, although no updates that I could find.

4out of 5Pat–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 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 these errors. Aside from being just thoroughly enjoyable to read, I particularly commend the author for wide net the book casts in terms of readership. It will certainly appeal to working scientists, but I think also it will be enjoyed and understood by those non-specialist interested in science or statistics as a whole. Additionally, I think this would make a very nice supplemental reading text for an introductory stat course. I plan on doing this next time I'm schedule to teach such a course. Overall, I would say this is something that just about everybody should read. Unimportant closing points: For a first edition the book is impressively free of errors and clunkiness. Also, the print edition is very nicely bound on high quality paper.

4out of 5Ari–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 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 background; despite this, the examples of mistakes tend to be drawn widely from the social and biological sciences, especially medicine. My sense is that this is necessary -- we need statistics a lot more in the life and social sciences than in the physical sciences. In physical science, we can typically scale the experiment up to the point where statistical error is insignificant; in the life or social sciences, often experimental size is more narrowly limited.

5out of 5Christina Jain–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.

4out of 5Bastian Greshake Tzovaras–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).

5out of 5Amy–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.

5out of 5Clintweathers–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.

4out of 5Sophia–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 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 statistics done wrong, since these stick in your memory, and drive home the point of how unreliable some reported "statistics" really are. There are also a few good suggestions on what you can do differently to avoid some of these pitfalls, but it's not really sufficient to put any changes in practice. The major fault with this book is that it's actually pretty bad at explaining things. In a certain sense you need to already be familiar with some statistics concepts, and even then, sometimes his explanations leave you more confused then enlightened. Case in point, the definition of p-values. He states that a p-value is "the probability, under the assumption that there is no true effect or difference, of collecting data that shows a difference equal to or greater than the one observed." Then he provides a series of quiz true-or-false questions on what it means to have a p-value of .01, and surprise! they're all false. I spent at least an hour trying to figure out why some of them were false, and I'm not really sure I succeeded. The author, like many other statistics people, don't seem to have spent the time trying to identify where it is that students go wrong when they misunderstand a statistics concept, but just raise their hands in despair, despite the fact that all students are making roughly the same mistakes, meaning we all have the same misunderstanding that needs correcting. In this example, the statement that tripped me up the most as false was "there is a 1% probability that the null hypothesis is true", and "if you were to replicate the experiment 100 times, you would obtain a significant result in 99% of trials". The author was no help in explaining why these were wrong, and I'm still trying to figure it out, but the key factor seems to be the specification "under the assumption there is no effect" (thank you, wikipedia). Given that these two statements are wrong, it raises an interesting problem, because I think the reason scientists like p-values in the first place is because they assume it indicates their probability of being wrong, the probability that their results were due to chance. If this is not so, all the more reason to abandon the p-value. In sum, this is not the most helpful book in dealing with the murky waters of statistics, but it's better than nothing.

4out of 5Andrew Chen–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 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 mean zero effect. ch3: dependence between observations creates issues (1000 patients vs. 100 measurements of 10 patients). better experimental design or methods like hierarchical models might help address these concerns. ch4: remember meaning of p-value. p-value does not mean probability a given result is true/false--base rate also factors into the equation. multiple comparisons can result in false positives. ch5: one group being significant and another group being not significant != two groups are significantly different. comparing whether two intervals overlap is not a significance test. comparing many groups requires multiple comparison adjustments. ch6: using data to decide on analysis means new data should be collected to do the actual analysis. if separating groups based on some significance test, expect natural mean reversion. stopping rules can introduce bias and path dependency in experimentation. ch7: no need to bucket continuous variables unless there is some intuitive reason. instead, use techniques that are designed for continuous variables. if bucketing, bucket based on reasonable criteria, not to maximize statistical significance. ch9: try to have the statistical hypothesis in mind before data collection and testing to minimize bias.

5out of 5Angela–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 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 significant results (green jelly beans!) with multiple comparisons, put negative or non-results in the "filing cabinet" and suffer from the "winner's curse" (where randomly large results are more likely to hit the p-value bingo and thus get reported, leading to an upward bias). In fact, EVERYTHING leads to an upward bias in results - as Ioannidis said, most research findings are probably false. Or, at least, not as big and positive as we so believe. I thought this would have a bit more practical stuff, a bit more Bayes (BAYES), and a bit of a wider scope. The last sections, on the perverse incentive structures of academia (pre-analysis plans that no one really signs up for; journals that reward "winner's cursey" BIG, POSITIVE results, p-hacking), were definitely interesting and got my fist shaking. But I'm not in that world anymore, and so I'm kinda like, "oh well, dudes". I mean, there is a LOT wrong with academia's incentive structures, and, yes, they definitely corrupt the pure Science, but what about practitioners in industry? Oh well.

5out of 5Erika RS–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 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 is more likely to be a true one, the magnitude is likely to be exaggerated since you'll only accept the data sets which show the effect very strongly. Better understanding of statistics and including those with formal statistical training as collaborators can help, but ultimately, the take away lesson from this book is that unless (and even when) you're looking at a result based on truly massive amounts of data, you should take any result as provisional until it's been replicated and replicated and replicated. My main criticism of this book is that it was an easy enough read that, a few weeks later, I feel myself having forgot most of the details of the statistical methods discussed in the first part of the book. Retention takes a bit more struggle, and this book didn't force the reader into that struggle.

5out of 5Deane Barker–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 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 society puts far too much faith in statistics, and scientific studies in general. In the last third of the book, the critiques become more general, and thus more interesting. You learn about the pressure that academics are under to publish, and why journals like to publish more interesting/extreme results, and how open access and digital-only journals are changing the nature of publishing. You also learn about the sometime sketchy things that scientists do. They hide their data, they hide their calculations, they "torture" data until it "confesses" something interesting, etc. If you read it, suffer through the math and some of the tedious esoteric stuff in the beginning. It gets more general, and more useful, later on.

4out of 5Fraser Kinnear–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 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 with dichotomization vs regression, and more. Also included is a lot of insight into the vagaries of science culture. One closes the book unsurprised that many scientific communities today face a “replication crisis” thanks to truth inflation and myriad other problems discussed. A working knowledge of statistics is definitely necessary to enjoy.

5out of 5Dana Kraft–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 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. My biggest takeaways are: - Bring plenty of humility into any serious data analysis effort. Nobody wants to think that their hard work was all for naught, and we can be pretty innovative at finding positive conclusions where non really exist - Statistics are not necessarily objective. Fair people with the right incentives can disagree on methods and therefore results. - A good statistical analysis requires a lot of subject matter expertise in whatever you're studying in addition to statistics. This seems obvious, but you can just "look at the data" and figure things out. - Most of the businesses I've worked with don't have nearly enough data to support any sort of rigorous statistical analyses, especially when you start slicing and dicing the data. - Stop reading news articles about scientific studies. Not sure I needed a reminder on this one. Specific things I liked: List of relevant questions for any analysis: 1) What do I measure? 2) Which variables do I adjust for? 3) Which cases do I exclude? 4) How do I define groups? 5) What about missing data? 6) How much data should I collect? Reminder about Hanlon's Razor: "Never attribute to malice that which is adequately explained by incompetence," "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."

5out of 5Geckoboard–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 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 audience, it’s full of references to scientific studies on the subject. In the final chapter, it explores how the scientific community in general can avoid doing statistics wrong. As well as improving the quality of your own research, it emphasizes the importance of scrutinizing other studies properly when the author’s ‘grasp of statistics is entirely unknown’ to you. Something to remember next time you’re confronted with spurious data in a meeting! We liked it so much we put our own review up here: https://goo.gl/6Jy6FB

5out of 5Adil–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 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 when the fact is that a very small proportion of us demonstrate consistent correct usage of even the p value, which has been sitting at the heart of our inferences for over a century. Overall, the author has done a great job and a valuable service to science. This book deserves the attention of all aspiring and current scientists. It will probably become mandatory reading for the students in my lab.

4out of 5Don–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 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 statistical errors, Reinhart also offers helpful tips on how to avoid them. I found myself convicted in several places as Reinhart described erroneous statistical practices (such as not adjusting for multiple comparisons) that I have repeated more than once. I will definitely be reviewing this book in the future as I work to better my own statistical practices and critically analyze the practices of other researchers. Despite the serious (and slightly boring sounding) topic, this book is a delight to read. The writing style is down-to-earth with some jokes sprinkled throughout. I really think this book should be read by not only every person in a serious technical position, but by laymen who, by learning the common pitfalls in science, can better question the fantastic results so often touted by the media. Highly recommended!