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Thinking Statistically
Thinking Statistically is the "sharp little book" that shows you how to think like a statistician, without worrying about formal statistical techniques. Along the way we learn how selection bias can explain why your boss doesn’t know he sucks (even when everyone else does); how to use Bayes’ Theorem to decide if your partner is cheating on you; and why Mark Zuckerberg should never be used as an example for anything. See the world in a whole new light, and make better decisions and judgements without ever going near a t-test. Think. Think Statistically.
136 pages, Paperback
First published October 22, 2011
About the author
Uri Bram
10 books18 followersUri Bram writes popular non-fiction with a conceptual approach to mathematical, scientific and analytical thinking.
Bram's first book, Thinking Statistically, shows how to use key statistical concepts informally in everyday life: it has been described as an "excellent read about basic statistical issues... very accessible to even those without a math background," "a great introductory primer, a good basis point to go deeper, or a short read that's humorous," and "a small gem, highly recommended."
Bram's first book, Thinking Statistically, shows how to use key statistical concepts informally in everyday life: it has been described as an "excellent read about basic statistical issues... very accessible to even those without a math background," "a great introductory primer, a good basis point to go deeper, or a short read that's humorous," and "a small gem, highly recommended."
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Displaying 1 - 30 of 57 reviews
November 12, 2020
This is a book about statistics in plain English with silly humor and zombie stories. It's a good idea. I am familiar with these topics already, so I'm having trouble imagining how it works for an adult who doesn't know science. A short, simple book can't cover everything, but even so, the content left me somewhat confused. For example, if you don't understand how scientists figure out what causes what, I don't think you'll be much better off after reading a chapter that ends in "The world rarely gives us easy problems with easy answers."
Other topics are problematic too. Bayesian analysis is illustrated with the Sally Clark story about a rush to convict a woman of murder because she had multiple babies who died of SIDS. Bram's point is that murder is rare, so one shouldn't assume it's common. OK, fine. But if one is going to use that story, then one also has to cover the opposite story from Syracuse about the woman who really did murder multiple babies but people there thought it was SIDS, and that led to a rush that took SIDS science in the U.S. down the wrong path for many years. The Death of Innocents: A True Story of Murder, Medicine, and High-Stake Science
The real lesson from both these stories is that you need to investigate and get the facts, instead of making assumptions and rushing to conclusions. And you should try to test your hypotheses using the best available evidence.
At the end, Bram recommends other books and I would agree with him on that. Bad Science How to Lie with Statistics
Otherwise, for learning about some of the underlying topics, I would recommend this beautiful book:Investigating Disease Patterns: The Science of Epidemiology
Other topics are problematic too. Bayesian analysis is illustrated with the Sally Clark story about a rush to convict a woman of murder because she had multiple babies who died of SIDS. Bram's point is that murder is rare, so one shouldn't assume it's common. OK, fine. But if one is going to use that story, then one also has to cover the opposite story from Syracuse about the woman who really did murder multiple babies but people there thought it was SIDS, and that led to a rush that took SIDS science in the U.S. down the wrong path for many years. The Death of Innocents: A True Story of Murder, Medicine, and High-Stake Science
The real lesson from both these stories is that you need to investigate and get the facts, instead of making assumptions and rushing to conclusions. And you should try to test your hypotheses using the best available evidence. At the end, Bram recommends other books and I would agree with him on that. Bad Science How to Lie with Statistics
Otherwise, for learning about some of the underlying topics, I would recommend this beautiful book:Investigating Disease Patterns: The Science of Epidemiology
April 27, 2014
I like the author's writing style, he's funny and informal but his jokes don't get in the way of understanding the concepts which he teaches in this book.
The stories and examples are great and really aid in understanding the subjects of the book, for me the Sherlock Holmes reference was key to understand better the Bayes' theorem.
I got tired of pop psychology / economics books that feel bloated with too much text and in the end you think that the book would be more enjoyable with half of the size.
This book is small but I finished it with a better understanding of statistics and it made me think a lot about how the world works.
The stories and examples are great and really aid in understanding the subjects of the book, for me the Sherlock Holmes reference was key to understand better the Bayes' theorem.
I got tired of pop psychology / economics books that feel bloated with too much text and in the end you think that the book would be more enjoyable with half of the size.
This book is small but I finished it with a better understanding of statistics and it made me think a lot about how the world works.
December 19, 2012
This is a delightful little book (just three chapters) introducing three of the fundamental aspects of statistics that can get us confused: selection bias, edogeneity (effectively missing external factors which are influencing the outcome) and the use of Bayesian statistics, an approach that is very powerful but makes it easy to go astray.
I wouldn’t quite describe this as a popular science book – there are probably rather too many equations – but it is excellent both as providing a bit of understanding for those making use of statistical methods (it’s all too easy to just crank the handle without understanding what you are doing and thereby come up with the wrong results) and as an introduction for the general reader who isn’t put off by a little bit of jargon and equations in what is, nonetheless, a very readable little book.
Thinking Statistically is short enough to read in a couple of hours, and I think it’s a credit to the author that I thought ‘Oh, really, I wanted more!’ when I got to the end. Uri Bram’s aim is to get the reader taking a more statistical viewpoint. Not necessarily wheeling out the statistical big guns every time you make a decision, but at least being aware of the statistical processes you are undergoing mentally, often unconsciously.
If you would like to know a bit more about statistics, but find the whole business a bit baffling, this is a good place to start.
You may wonder what the cover has to do with statistics. So did I. The simple answer is nothing.
Review first published on www.popularscience.co.uk and reproduced with permission
I wouldn’t quite describe this as a popular science book – there are probably rather too many equations – but it is excellent both as providing a bit of understanding for those making use of statistical methods (it’s all too easy to just crank the handle without understanding what you are doing and thereby come up with the wrong results) and as an introduction for the general reader who isn’t put off by a little bit of jargon and equations in what is, nonetheless, a very readable little book.
Thinking Statistically is short enough to read in a couple of hours, and I think it’s a credit to the author that I thought ‘Oh, really, I wanted more!’ when I got to the end. Uri Bram’s aim is to get the reader taking a more statistical viewpoint. Not necessarily wheeling out the statistical big guns every time you make a decision, but at least being aware of the statistical processes you are undergoing mentally, often unconsciously.
If you would like to know a bit more about statistics, but find the whole business a bit baffling, this is a good place to start.
You may wonder what the cover has to do with statistics. So did I. The simple answer is nothing.
Review first published on www.popularscience.co.uk and reproduced with permission
August 19, 2018
A good primer to some of the statistical concepts that everyone needs to understand. Nonetheless, a careful reader will have to consult some more resources in order to grasp the concept. Very important as a start but not nearly as sufficient.
August 15, 2019
A 3 chapter book: Selection Bias, Models, Bayes.
This fits in the now-popular mental models genre of books (Rolf Dobelli, Buster Benson, Charlie Munger).
Whereas other authors tend to go into long lists of models, Bram just does a really good job explaining three.
In particular, his chapter on Bayes and how to use Bayes in day-to-day life was excellent.
This fits in the now-popular mental models genre of books (Rolf Dobelli, Buster Benson, Charlie Munger).
Whereas other authors tend to go into long lists of models, Bram just does a really good job explaining three.
In particular, his chapter on Bayes and how to use Bayes in day-to-day life was excellent.
May 30, 2019
A wonderful introduction to statistics. It tackles 3 major issues within statistics to give you an overview of the field.
The first one is selection bias. How we can take a non-random sample, but act as if it was random. The second is about models. How we simplify the world in abstract terms, and how that translates to equations. On the way, touching on things like random variation, correlation, survivorship bias, reverse causation, and others. Finally, it introduces Bayes' Theorem - the importance of updating probabilistic assessments based on new information but taking into account the probabilities of different hypotheses as well.
It's perhaps one of the best books I have read, despite its simplicity. The writing is excellent, and everything flows extremely well. Also unexpectedly funny, which made the reading much more enjoyable. My only complaint would be that it's extremely short, it can be read in an hour or two. Being an introduction, it's definitely a positive to be short, but I felt with the author's wonderful writing and being able to simplify concepts, I doubt anyone would mind having double the content. I was hoping that the author had another, and longer, book on statistics. But unfortunately, that wasn't the case. Hopefully he writes one in the future, but he does provide some book recommendations at the end.
If you're looking for a basic introduction to statistics, it's a highly recommended read.
The first one is selection bias. How we can take a non-random sample, but act as if it was random. The second is about models. How we simplify the world in abstract terms, and how that translates to equations. On the way, touching on things like random variation, correlation, survivorship bias, reverse causation, and others. Finally, it introduces Bayes' Theorem - the importance of updating probabilistic assessments based on new information but taking into account the probabilities of different hypotheses as well.
It's perhaps one of the best books I have read, despite its simplicity. The writing is excellent, and everything flows extremely well. Also unexpectedly funny, which made the reading much more enjoyable. My only complaint would be that it's extremely short, it can be read in an hour or two. Being an introduction, it's definitely a positive to be short, but I felt with the author's wonderful writing and being able to simplify concepts, I doubt anyone would mind having double the content. I was hoping that the author had another, and longer, book on statistics. But unfortunately, that wasn't the case. Hopefully he writes one in the future, but he does provide some book recommendations at the end.
If you're looking for a basic introduction to statistics, it's a highly recommended read.
April 3, 2018
The book was well worth the read for a beginner. It took me around 1 hour and 20 minutes to read the whole thing, and came out with around a dozen insights.
The book's a great read for beginners to statistics. Even if you have a firm grasp on the basics of statistics, the book's a good review on it.
It was mostly a book teaching you how to think statistically (no way!) rather than a guide to statistics. It covers selection bias, base line fallacy, a bit of Bayesian statistics, some logical fallacies, and a bunch of other things.
A good way to spend 80 minutes.
The book's a great read for beginners to statistics. Even if you have a firm grasp on the basics of statistics, the book's a good review on it.
It was mostly a book teaching you how to think statistically (no way!) rather than a guide to statistics. It covers selection bias, base line fallacy, a bit of Bayesian statistics, some logical fallacies, and a bunch of other things.
A good way to spend 80 minutes.
November 8, 2012
"Thinking Statistically" is a nice, thin intro to core concepts of statistics that apply most directly to everyday life: selection, endogeneity (distortions caused by hidden or missing causative links) and Bayes' Theorem. It fills the gap between simple one page introductions to stats and the complexity of an introductory statistics course. The examples are generally well chosen to illustrate the concepts in a concrete manner,
Although I've done some amount of stats in my time, and I was quite familiar with all of the concepts in the book, I was looking for simple examples that I could refer to, both in explaining them to others, and to solidifying the foundational ideas in my own mind. I am primarily interested in making Bayes a more central part of my thought process, and help from books like this moves me in that direction.
I've recommended this book to my teen daughters who have yet to take statistics courses as a way to introduce them to statistical concepts that should part of anyone's intellectual toolbox in our data-driven times. It is refreshing to have books than can be read in a couple of hours and don't try to do more than they need to in an effort to fill 200 pages. The examples based on familiar situation also connect the material with the reader's experiences.
My primary criticism is that I think the author switches levels rather abruptly. After using language and explanations that would draw in even a non-mathematical thinker, he introduces equations that might be confusing for those rusty on their algebra or algebraic notation. None of these are particularly gnarly equations. However, a notation like: P(Hypothesis|Evidence) which someone familiar with the notation would read as "the probability of the hypothesis given the evidence" would not be accessible of one was not familiar with P() or with | . Please understand that this is not a major problem, and that anybody who has taken or is taking a stats course will have no difficulty with the notation. The only reason I make this point is that with some effort to simplify the mathematical notation, the rest of the book would make these concepts available to an even broader range of readers.
Overall, if you want it a quick refresher/intro to the big 3 concepts in stats, I can recommend this book.
Although I've done some amount of stats in my time, and I was quite familiar with all of the concepts in the book, I was looking for simple examples that I could refer to, both in explaining them to others, and to solidifying the foundational ideas in my own mind. I am primarily interested in making Bayes a more central part of my thought process, and help from books like this moves me in that direction.
I've recommended this book to my teen daughters who have yet to take statistics courses as a way to introduce them to statistical concepts that should part of anyone's intellectual toolbox in our data-driven times. It is refreshing to have books than can be read in a couple of hours and don't try to do more than they need to in an effort to fill 200 pages. The examples based on familiar situation also connect the material with the reader's experiences.
My primary criticism is that I think the author switches levels rather abruptly. After using language and explanations that would draw in even a non-mathematical thinker, he introduces equations that might be confusing for those rusty on their algebra or algebraic notation. None of these are particularly gnarly equations. However, a notation like: P(Hypothesis|Evidence) which someone familiar with the notation would read as "the probability of the hypothesis given the evidence" would not be accessible of one was not familiar with P() or with | . Please understand that this is not a major problem, and that anybody who has taken or is taking a stats course will have no difficulty with the notation. The only reason I make this point is that with some effort to simplify the mathematical notation, the rest of the book would make these concepts available to an even broader range of readers.
Overall, if you want it a quick refresher/intro to the big 3 concepts in stats, I can recommend this book.
May 21, 2016
Very nice! Bram's brief book explains selection effects, causality and Bayes to lay audience.
I can't really rate this correctly - I think it written in a clear and entertaining way. For me (working in this field) the benefit was in getting a few nice examples to use when explaining some of these concepts, rather then learning anything new. But I think think scientifically minded people could enjoy it and learn a few interesting concepts past the standard "skeptics" fare of Occam's razor etc.
One failing: some examples were nice, but some were awful (boy who hits people after sneezing?), reminding me of artificial, boring, impractical examples from textbooks.... it's a pity. Also graphical examples could have been leveraged far more, Bram kept it at the most rudimentary.
One more thought: thank god for the electronic format - the book is ~78 pages. If this was a paper book, the publisher would be compelled to pad this to 250 pages with fluff. As it is it now, it is short, interesting and educational.
I can't really rate this correctly - I think it written in a clear and entertaining way. For me (working in this field) the benefit was in getting a few nice examples to use when explaining some of these concepts, rather then learning anything new. But I think think scientifically minded people could enjoy it and learn a few interesting concepts past the standard "skeptics" fare of Occam's razor etc.
One failing: some examples were nice, but some were awful (boy who hits people after sneezing?), reminding me of artificial, boring, impractical examples from textbooks.... it's a pity. Also graphical examples could have been leveraged far more, Bram kept it at the most rudimentary.
One more thought: thank god for the electronic format - the book is ~78 pages. If this was a paper book, the publisher would be compelled to pad this to 250 pages with fluff. As it is it now, it is short, interesting and educational.
June 1, 2012
Excellent read about basic statistical issues. Very accessible to even those without a math background.
The author covers three main issues:
1) Selection Bias
2) Endogenity (When something in the error measure you use contains a variable that is related to the thing you are measuring)
3) Bayes Theorem
A good pick up for those looking to review some key terms during a statistical course; or for those just looking to familiarize themselves more with the issues above.
Some down-to-earth examples are used that make the review fun. The author uses relationships; legal cases; and even zombies to make the topic easier to understood.
The author covers three main issues:
1) Selection Bias
2) Endogenity (When something in the error measure you use contains a variable that is related to the thing you are measuring)
3) Bayes Theorem
A good pick up for those looking to review some key terms during a statistical course; or for those just looking to familiarize themselves more with the issues above.
Some down-to-earth examples are used that make the review fun. The author uses relationships; legal cases; and even zombies to make the topic easier to understood.
June 9, 2012
Great value for money. A short 2-hour read that introduces three key statistical concepts (and common pitfalls): Selection Bias, Endogeneity and the Bayes Theorem. The language is very simple and Bram's style is very funny. It was a small gem, highly recommended.
January 21, 2019
Not a bad book to listen to while walking, very short, just 1 1/2 hours. The author has two audiences, 1. People who need an introduction to statistical thinking 2. People who want a nontechnical verbal reinforcement of some essential ideas of statistical thinking. The author's menu of "essential" ideas include endogeneity, Bayes reasoning/reasoning on condition/base rate fallacy, with an overall brief of statistical thinking in the first 20 mins using everyday examples. Only get this book if you're paying roughly $3 or less. Professional statisticians (PhDs) may find this book useless, but definitely not a total waste of time for practitioners, who may value a verbal restatement of some basic ideas
January 22, 2017
A Christmas present from my daughter. This short (70 page) book is a fun introduction to the key statistical concepts of selection, endogeneity and Bayes Theory. It tries very hard to get across some quite tricky concepts with humour and nicely chosen examples, and I think it succeeds. These are the key things to watch out for in any media published statistics - the concept of bias through self selection, the risk of a result having a dependence on a non-random variable not considered, and making sure that the computed probability of an outcome considers all possible hypothesis and any new information. Enjoyable and refreshing couple of hours study.
December 9, 2025
I liked the book, he's a clear and witty writer. His use of geometry to illustrate Bayes is very useful. His endogeneity chapter, while relevant and useful is now covered by the idea of directed acyclic graphs and do-calculus.
The book is interesting to read in that it shows how statistics is currently undergoing a massive revolution led by people like Judea Pearl and Richard McElreath which this book didn't get the chance to capture. I wonder what the book would've looked like from this modern perspective?
The book is interesting to read in that it shows how statistics is currently undergoing a massive revolution led by people like Judea Pearl and Richard McElreath which this book didn't get the chance to capture. I wonder what the book would've looked like from this modern perspective?
August 2, 2018
Mình vote cuốn này điểm tối đa. Cách viết của tác giả khá hay, kiểu vừa hài hước vừa thông minh, đưa ra các ví dụ khá là ổn đối với những người ko chuyên về thống kê như mình. Cuốn này trẻ con cũng có thể đọc được để tiếp cận với thống kê. Có 3 điểm mà tác giả đưa ra, có thể luôn đúng khi bắt đầu vào việc:
-Selection
-Endogeneity (distortions caused by hidden or missing causative links)
-Bayes' Theorem
Sẽ ghi nhớ <3
-Selection
-Endogeneity (distortions caused by hidden or missing causative links)
-Bayes' Theorem
Sẽ ghi nhớ <3
October 27, 2019
A very nice introduction to statistical thinking. I read this book while preparing for a "Foundations in Applied Statistics" course and it was helpful.
It is a short and entertaining book, which can be read in a few hours. However, it needs to be complemented by other readings providing a technical approach towards statistics.
It is a short and entertaining book, which can be read in a few hours. However, it needs to be complemented by other readings providing a technical approach towards statistics.
June 16, 2026
This was great though I wish it was longer. I made the mistake of listening to the audiobook and wish I had read it. Despite Uri warning me that the actual equations didn't matter as soon as they started happening I panicked. I stopped understanding math in school when we introduced letters along with numbers. I think if I had them in front of me I could have understood them or not cared as much.
July 20, 2020
A very short book but a nice introduction to statistical thinking. It exposes some unseen but obvious human biases. I think it would be nice to read it before reading "How to Lie with Statistics" by Darrell Huff, or "Thinking Fast, and Slow" by Daniel Kahneman.
October 4, 2024
Fun idea for sure, but when you are setting your sights on explaining three statistical concepts (sampling bias, endogeneity, and Bayes' theorem) and endogeneity is hard to understand, and Bayes is incomprehensible, maybe the book needs to be longer.
February 6, 2018
This is a delicious, bite-sized introduction to sampling bias and Bayes’ Theorem. The only reason I’m not giving it a 5 is because it’s so short.
April 29, 2019
Useful
This is an intuitive introduction to the world of statistics. It is well written and concise which might be the best part about it. Worth the money and time.
This is an intuitive introduction to the world of statistics. It is well written and concise which might be the best part about it. Worth the money and time.
September 28, 2019
This is a great, *very intro* level book into statistical concepts - sampling bias, Bayes theorem, and other high level concepts. Great to get ones foot in the door.
February 1, 2021
Very short but entertaining
A humorous explanation of some core tenants in statistics. A good short read that will make you think just a touch differently.
A humorous explanation of some core tenants in statistics. A good short read that will make you think just a touch differently.
September 7, 2022
I wanted to like this book. Mediocre.
August 17, 2025
Breezy introduction to some important statistical concepts. A little light for my taste, but a quick read. (3.5)
May 25, 2026
A very basic book on statistics. Great as an introduction.
July 21, 2019
Read to this one now and really enjoying it. I love writing style and everything Describe in this book.
May 12, 2013
Tres capítulos para un libro de 80 páginas. No mucho para leer, pero muy interesante y entretenido. Cada capítulo tiene un concepto fundamental que nos tiene que servir a la hora de poner números en contexto: el sesgo de selección, los problemas endógenos (también llamado sesgo de variable omitida)y la correcta aplicación del teorema de Bayes. Hay unos cuantos ejemplos repartidos por el libro de números que a primera vista parecen una cosa y pero resultan ser otra en cuanto les soltamos un poco de pensamiento bien orientado. Muy, muy interesante.
Un ejemplo: tenemos un test que detecta si una persona está enferma de gripe aviar con un 99% de fiabilidad. Es decir, el 99% de los enfermos que se hagan la prueba van a dar positivo y el 99% de los sanos que se hagan la prueba van a dar negativo. Sabemos que más o menos una de cada 10.000 personas tiene la enfermedad. Y das positivo en en test. ¿Qué probabilidad hay de que realmente tengas la enfermedad? ¿Un 99%? Eres víctima de un mal uso del teorema de Bayes. En realidad es un 10%. El libro te explicará el porqué :)
Un ejemplo: tenemos un test que detecta si una persona está enferma de gripe aviar con un 99% de fiabilidad. Es decir, el 99% de los enfermos que se hagan la prueba van a dar positivo y el 99% de los sanos que se hagan la prueba van a dar negativo. Sabemos que más o menos una de cada 10.000 personas tiene la enfermedad. Y das positivo en en test. ¿Qué probabilidad hay de que realmente tengas la enfermedad? ¿Un 99%? Eres víctima de un mal uso del teorema de Bayes. En realidad es un 10%. El libro te explicará el porqué :)
October 7, 2014
Very simple book, easy to read.
It describes many situations where you should be using a statistical thinking through 3 chapters. Good introduction for someone completely new to the subject, and /or without a mathematical background.
First chapters is about selection (bias). Examples like the 1948 presidential election, where selection bias ruined the prediction of the outcome of the election. The biased was towards the people in the surveys ( by phone) , mainly republicans and not representative of the whole population
Second chapter is about endogeneity. Main idea is that correlation does not imply causation.
Third chapter talks about Bayes, and how you can improve your predictions based on plausible priors
It describes many situations where you should be using a statistical thinking through 3 chapters. Good introduction for someone completely new to the subject, and /or without a mathematical background.
First chapters is about selection (bias). Examples like the 1948 presidential election, where selection bias ruined the prediction of the outcome of the election. The biased was towards the people in the surveys ( by phone) , mainly republicans and not representative of the whole population
Second chapter is about endogeneity. Main idea is that correlation does not imply causation.
Third chapter talks about Bayes, and how you can improve your predictions based on plausible priors
July 3, 2015
This books is the Strunk & White of Statistics! Thinking Statistically, was originally gifted to me by a statistician when I embarked on a research project. It is an excellent start to the math of statistics because it gets right to the useful part of the matter. I later followed up with many other books and courses in the math of statistical analysis but I often returned to this book to 'ground myself' on how to use the new tools. I highly recommend this book to anyone who needs to perform statistical analysis.
Displaying 1 - 30 of 57 reviews