The definitive guide to statistical thinking Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even The definitive guide to statistical thinking Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

# The Art of Statistics: How to Learn from Data

The definitive guide to statistical thinking Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even The definitive guide to statistical thinking Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

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4out of 5Tim Roast–When I am not writing witty and informative reviews on Goodreads/Amazon my day job is as a Government statistician. Therefore when offered the opportunity to read this book I thought it would be useful for me to do so. And I do believe it is helping me in my work. I am thinking more about how best to present my statistics and what analytical techniques I could use too. So this book works from that perspective. This book takes real world questions and shows you how they've been answered introducin When I am not writing witty and informative reviews on Goodreads/Amazon my day job is as a Government statistician. Therefore when offered the opportunity to read this book I thought it would be useful for me to do so. And I do believe it is helping me in my work. I am thinking more about how best to present my statistics and what analytical techniques I could use too. So this book works from that perspective. This book takes real world questions and shows you how they've been answered introducing various statistical techniques as it does so. It does this whilst aiming to avoid "getting embroiled in technical details". The questions picked are quite interesting subjects like "why do old men have big ears?", "how many trees are there in this planet?" (an estimated 3.04 trillion if you must know) or what height will a son/daughter be given their parents' heights and so on with some of the questions being based on work the author has been involved in during his career. Relating the problems to real life helps make the text appeal not only to statisticians (to which this book is dedicated) but also to non-technical readers "who want to be more informed about the statistics they encounter both in their work and in everyday life." Some of this is not new stuff, e.g. early bits on presentation of data such as 3D pie charts not being useful for comparing proportions. But the book does get more involved as you work through it getting deeper in statistical techniques making it harder to understand and requiring more concentration, and the author is aware of this, for example asking if it is "all clear? If it isn't then please be reassured that you have joined generations of baffled students". Also the conclusion congratulates you for getting to the end. Useful stuff in here for me was the chapter on regression (which is what I use more commonly than much of the rest), and the last couple of chapters after the hard stuff were good reading too, showing bad examples and good examples of statistics from journals and the like and explaining why (offering learning points). Technical stuff is relegated to the technical glossary so this book is readable (which is good for a book about statistics), although still hard in places. For my work it has been useful and I'm glad I read it and have it for future reference.

4out of 5Vuk Trifkovic–Pretty good, but there are a few chapters where the author basically goes "I'm not explaining this very well, but I know you won't get it so let's just move on". I also wish there were a few more "digital" / web analytics cases, but that's just because it would help me. Overall, an interesting and useful read. Pretty good, but there are a few chapters where the author basically goes "I'm not explaining this very well, but I know you won't get it so let's just move on". I also wish there were a few more "digital" / web analytics cases, but that's just because it would help me. Overall, an interesting and useful read.

5out of 5Vysloczil–This amazing piece can somewhat be seen as the equivalent of Angrist&Pischke's "Mastering Metrics" for bread and butter statistical problems instead of intuitive econometrics. It covers everything one has to know when it comes to scientific studies that rely on data. All aspects and elements are touched, but math and formulas are relegated to an appendix. Thus the book is well suited for experts with year-long experience, college students of all fields, but especially science writers or people t This amazing piece can somewhat be seen as the equivalent of Angrist&Pischke's "Mastering Metrics" for bread and butter statistical problems instead of intuitive econometrics. It covers everything one has to know when it comes to scientific studies that rely on data. All aspects and elements are touched, but math and formulas are relegated to an appendix. Thus the book is well suited for experts with year-long experience, college students of all fields, but especially science writers or people that want to be well equipped when it comes to discussing or questioning the newest "study x found that y prevents cancer" headline. Explains concepts with easy-to-grasp real world examples, appealing to the intuition of the reader. Touches upon all topics, from basic proportions, regression, classification/"big data", up to bayesian approaches. Also covering common misconceptions and fallacies on the fly ("how to lie with stats"). Everything in a very coherent and readable way. A truly joyful read! Can be assign as a companion text for a stats undergrad course across all disciplines in order to show students, sometimes drowning in pure formula memorisaation, the beauty of stats and numbers and data. Also suited for AP stats people. Also for skilled professionals as a revision. A big plus is the companion code for the open-source software R, which together with Python is going to be the future of (statistical) programming. The last part of the book explains the so-called "statistical crisis in science" (or "replication crisis") and how it came about and, most importantly, what to do about it. Communication chains are analysed to understand how exaggerated newspaper headlines are created. Mot importantly the author provides check lists for the reader to be able to infer himself whether, or how much, a certain study or headline should be trusted.

4out of 5Bari Dzomba–I didn't like the first 60% of the book. It was too dumbed down even for me and not enough original storytelling for explaininf concepts to non math students. I even gave this feedback to the author. The last 1/3 of the book was much better,getting into p hacking, data quality, and data ethics. I didn't like the first 60% of the book. It was too dumbed down even for me and not enough original storytelling for explaininf concepts to non math students. I even gave this feedback to the author. The last 1/3 of the book was much better,getting into p hacking, data quality, and data ethics.

5out of 5Emil O. W. Kirkegaard–Very nice overall, not much algebra but focus on the reasoning behind, interesting examples. Good for nonscientists.

5out of 5Moh. Nasiri–Statisticians study patterns in data to help us answer questions about the world. When reported accurately, statistical research can enrich storytelling and inform the public about important issues. Unfortunately, there are a great many distorting filters that research has to pass through before it reaches the public, including scientific journals and the media. As statistical data creeps into our lives more and more, there is a growing need for us all to improve our data literacy so we can appr Statisticians study patterns in data to help us answer questions about the world. When reported accurately, statistical research can enrich storytelling and inform the public about important issues. Unfortunately, there are a great many distorting filters that research has to pass through before it reaches the public, including scientific journals and the media. As statistical data creeps into our lives more and more, there is a growing need for us all to improve our data literacy so we can appropriately assess the findings. Actionable advice: Don’t take statistics at face value. View statistical information the way you might view your friends: they’re the source of some great stories, but they’re not always the most accurate. Statistical information should be treated with the same skepticism you apply to other kinds of claims, facts and quotes. And, where possible, you should examine the sources of statistics behind the headlines so you can assess how accurately the information has been reported. ---- What’s in it for me? Improve your data literacy and learn to see the agenda behind the numbers. You might think that with the growing availability of data and user-friendly statistical software that does the mathematical heavy-lifting for you, there’s less need to be trained in statistical methods. But the ease with which data can now be accessed and analyzed has led to a rise in the use of statistical figures and graphics as a means of furnishing supposedly objective evidence for claims. Today, it’s not just scientists who make use of statistics as evidence, but also political campaigns, advertisements, and the media. As statistics are separated from their scientific basis, their role is changing to persuade rather than to inform. And the people generating such statistical claims are not necessarily trained in statistical methods. An increasingly diverse number of sources produce and distribute statistics with very little oversight to ensure their reliability. Even when data is produced by scientists undertaking research, errors and distortions of statistical claims can occur at any point in the cycle – from flaws in the research to misrepresentations by the media and the public. So, in today’s world, data literacy has become invaluable in order to accurately evaluate the credibility of the myriad news stories, social media posts, and arguments that use statistics as evidence. These blinks will give you all the tools you need to better assess the statistics you encounter on a daily basis. In this book, you’ll learn: how statistics can be used to catch serial killers; whether drinking alcohol is good for your health or not; and which remarkable creature can respond to human emotions even after it has died. ---- Statistics can help us answer questions about the world. Have you ever wondered what statisticians actually do? To many, statistics is an esoteric branch of mathematics, only slightly more interesting than the others because it makes use of pictures. But today, the mathematical side of statistics is considered only one component of the discipline. Statistics deals with the entire lifecycle of data, which has five stages which can be summarized by the acronym PPDAC: Problem, Plan, Data, Analysis, and Conclusion. The job of a statistician is to identify a problem, design a plan to solve it, gather the relevant data, analyze it, and interpret an appropriate conclusion. Let’s illustrate how this process works by considering a real-life case that the author was once involved in: the case of the serial killer Harold Shipman. With 215 definite victims and 45 probable ones, Harold Shipman was the United Kingdom’s most prolific serial killer. Before his arrest in 1998, he used his position of authority as a doctor to murder many of his elderly patients. His modus operandi was to inject his patients with a lethal dose of morphine and then alter their medical records to make their deaths look natural. The author was on the task force set up by a public inquiry to determine whether Shipman’s murders could have been detected earlier. This constitutes the first stage of the investigative cycle – the problem. The next stage – the plan – was to collect information regarding the deaths of Shipman’s patients and compare this with information regarding other patient deaths in the area to see if there were any suspicious incongruities in the data. The third stage of the cycle – data – involves the actual process of collecting data. In this case, that meant examining hundreds of physical death certificates from 1977 onwards. In the fourth stage, the data was analyzed, entered into software, and compared using graphs. The analysis brought to light two things: First, Shipman’s practice recorded a much higher number of deaths than average for his area. Second, whereas patient deaths for other general practices were dispersed throughout the day, Shipman’s victims tended to die between 01:00 p.m. and 05:00 p.m. – precisely when Shipman undertook his home visits. The final stage is the conclusion. The author’s report concluded that if someone had been monitoring the data, Shipman’s activities could have been discovered as early as 1984 – 15 years earlier – which could have saved up to 175 lives. So, what do statisticians do? They look at patterns in data to solve real-world problems. --- What to read next: How to Lie with Statistics, by Darrell Huff We’ve seen how statistical claims can be distorted in their passage from research to the public ear. Usually, these distortions of the data are unintentional and arise from a misunderstanding of statistical methods. Sometimes, however, these distortions are quite deliberate. The blinks to How to Lie with Statistics, by author Darrell Huff, deal with this darker side of statistics. They introduce the techniques that media and advertisements use to alter how data is perceived and interpreted. They also go deeper into some familiar themes, such as the difficulty of truly random sampling, the error of inferring cause from correlation, and the misuse of averages. To avoid getting fooled, head on over to our blinks on How to Lie with Statistics. Ref: blinkist.com

4out of 5☘Misericordia☘ ⚡ϟ⚡⛈⚡☁ ❇️❤❣–Q: A classic example of how alternative framing can change the emotional impact of a number is an advertisement that appeared on the London Underground in 2011, proclaiming that ‘99% of young Londoners do not commit serious youth violence’. These ads were presumably intended to reassure passengers about their city, but we could reverse its emotional impact with two simple changes. First, the statement means that 1% of young Londoners do commit serious violence. Second, since the population of Lon Q: A classic example of how alternative framing can change the emotional impact of a number is an advertisement that appeared on the London Underground in 2011, proclaiming that ‘99% of young Londoners do not commit serious youth violence’. These ads were presumably intended to reassure passengers about their city, but we could reverse its emotional impact with two simple changes. First, the statement means that 1% of young Londoners do commit serious violence. Second, since the population of London is around 9 million, there are around 1 million people aged between 15 and 25, and if we consider these as ‘young’, this means there are 1% of 1 million or a total of 10,000 seriously violent young people in the city. This does not sound at all reassuring, (c) Q: But these are generally reported as the ‘average house price’, which is a highly ambiguous term. Is this the average-house price (that is, the median)? Or the average house-price (that is, the mean)? A hyphen can make a big difference. (c)

4out of 5Florian–I really wanted to like this book. But at times it felt like it’s trying to cover too much ground and a lot of it not deep enough. Often times more technical details would have aided proper understanding of the subject. It was also quite surprising to see supervised learning being defined as classification, which seems incorrect and also doesn’t explain what supervised learning actually is.

4out of 5Daniel B-G–I never really got statistics when I did Maths when I was younger. The most esoteric parts of pure maths were a breeze, but statistics never clicked, in large part because nobody was able to explain to me what some of the core concepts actually mean. Chief villain in the piece is standard deviation, something I considered to be the height of charlatanism. Fast forward 20 years, and I am working in a role that actually needs to know statistics, and I'm regretting my youthful intransigence. This bo I never really got statistics when I did Maths when I was younger. The most esoteric parts of pure maths were a breeze, but statistics never clicked, in large part because nobody was able to explain to me what some of the core concepts actually mean. Chief villain in the piece is standard deviation, something I considered to be the height of charlatanism. Fast forward 20 years, and I am working in a role that actually needs to know statistics, and I'm regretting my youthful intransigence. This book has, to a large part, undone the damage. This book is NOT a practical guide on how to do statistics. It IS a guide, something that shows you what statistics is good for, what it is not, the good and bad ways to practice it, and what each concept means. I can go and read any number of articles about how to do statistics, how to apply a particular technique, but all of them presuppose I know when I should and in what circumstances. That's where this book closes the gap. I suspect I'll need to return to this many times. But this book goes beyond just helping specialists to do statistics. It also helps people to interpret statistics. It gives you a good groundwork in the various different principles of statistics, without getting bogged down in calculation. It also includes a significant section critiquing how statistics are communicated to the public, and I think this would of interest to anyone. All in all, this is a very good book. I can't recommend it enough. If you have any interest in statistics, this should be on your shelf.

5out of 5James Miller–I read a lot of pop-maths books and enjoy them (Hannah Fry, Du Sautoy, Simon Singh, and pervious books by Spiegelhalter). This one is a bit more chewy. Where Sex by Numbers uses statistics to tell you things, this book is much closer to a textbook on how statistics should be done and what can be learned from it. I have learned a great deal from this and his discussions of Harold Shipman and of 95% accuracy tests giving far more false positives than accurate responses (inter alia) have been really I read a lot of pop-maths books and enjoy them (Hannah Fry, Du Sautoy, Simon Singh, and pervious books by Spiegelhalter). This one is a bit more chewy. Where Sex by Numbers uses statistics to tell you things, this book is much closer to a textbook on how statistics should be done and what can be learned from it. I have learned a great deal from this and his discussions of Harold Shipman and of 95% accuracy tests giving far more false positives than accurate responses (inter alia) have been really eye-opening. The technicality of p and t tests has got a bit beyond me and one or two graphs could be clearer (though my preview copy is not coloured and so perhaps this is unfair). Certainly one comes away from the book knowing why statistics and significance testing is becoming ever more central in subjects such as Psychology where a replication crisis is at work (and even at A-Level stats is becoming more prevalent) and his clear desire that the journalists reporting cases (he often cites examples of poor reporting) would understand teh data they use and not confuse themselves and readers. Huge amounts to learn, but perhaps too technical in places for most of us.

5out of 5Travis Valdez–As a data scientist, I enjoyed the non-technical aspects of this book more than the technical (though the review was welcome). Statistical training should include more courses and resources like this that remind us there is more to the practical use of statistics than just the mathematics. Publication, ethics, review, interpretation and communication all play a vital role in how studies benefit society at large. These concepts are more useful and accessible to the general population than, say, t As a data scientist, I enjoyed the non-technical aspects of this book more than the technical (though the review was welcome). Statistical training should include more courses and resources like this that remind us there is more to the practical use of statistics than just the mathematics. Publication, ethics, review, interpretation and communication all play a vital role in how studies benefit society at large. These concepts are more useful and accessible to the general population than, say, the formula for determining the p-value of a test.

5out of 5Arun Babji–The book dealt with the spirit of applying statistics. It has very apt examples, and a clear style of writing. Reading this book can help a great deal before the reader jumps into the mechanics of Machine Learning using various models. Concepts like the 6 principles of P values, types of uncertainty, bootstrapping as an equivalent of sampling with replacement, bagging as a bootstrapping method using multiple decision trees and a consensus prediction are explained very well.

4out of 5Clémentine–I don’t really know when I started Work/Reading but I really like it. This book was great and I am glad the author included this last chapter! Some parts may seem painful but overall it was very informative.

5out of 5Andrew Dalby–The clearest and best introduction to statistics written by one of the greatest living statisticians. This book does not dumb down the content it presents the latest thinking about data in a clear and accessible way. I would recommend this book to anyone who is really interested in learning about data and trying to separate facts from fiction, but it is also a perfect introductory text for an undergraduate statistics course for those who are afraid of statistics. It is a pleasure to read.

5out of 5Jacqueline–a really solid, math-free overview of statistics that was heavy on real world examples. if you want to ask smarter questions about the numbers/stats you see, this is a great book!

5out of 5Melanie H–Not to reveal my age, but I haven’t been in a math class since the late ‘90s, and as luck would have it, it was a stats class. However, with all the talk about following the science on mask wearing, eating in restaurants, keeping schools open, etc. I thought it was time to take myself to task. Hey, if every other armchair statistician was mouthing off on Facebook, why not take a dive into the stats myself? And so, I started with Spiegelhalter’s book ‘The Art of Statistics: How to Learn from Data Not to reveal my age, but I haven’t been in a math class since the late ‘90s, and as luck would have it, it was a stats class. However, with all the talk about following the science on mask wearing, eating in restaurants, keeping schools open, etc. I thought it was time to take myself to task. Hey, if every other armchair statistician was mouthing off on Facebook, why not take a dive into the stats myself? And so, I started with Spiegelhalter’s book ‘The Art of Statistics: How to Learn from Data” to see what I knew about the subject. Bonus points for its colorful cover. Let’s start with the big surprise, there is no single unifying theory of statistical inference (p. 305). Wait, what? Yes, turns out there are three competing approaches: Fisher, Neyman-Pearson, and Bayesian. I won’t bore you with the details, and let’s be realistic, I’d have to make some flashcards and get to memorizing before I could explain much about those differences, but let’s just say there are different approaches to drawing conclusions from data. I’m also happy to report that your average person, myself included, doesn’t understand probability and chance. Friendly reminder, luck has nothing to do with numbers. What appeals to me most about the discipline is the idea of transforming our life experiences into data. That we can draw inferences about general principles from specific examples is fascinating to me, as is the idea of overfitting. When the algorithms get too complex, we start fitting the noise rather than the signal. The goal is to find the signal in the noise, not to make the noise louder. To be honest, I skipped over some of the more complex theoretical sections. Since I’m not running any research studies or statistical analyses any time soon, I think the world is safe from my armchair interpretations. If you’re looking to revisit the subject or learn more about it for the first time, this book is a great entry point. It’s easy to read and filled with engaging real-life examples. As for my thoughts on what’s safe for the public during the pandemic and when, I’ll defer to the public health experts and epidemiologists. Because despite my reading and life experiences, I, like most other citizens, do not have the stats skills to look at the bigger picture. Certainly, I encourage you to read broadly from a variety of academic and popular sources, but let’s not kid ourselves, interpreting data is hard, and is best left to those who understand how to do it at an advanced level, especially when it comes to matters of life and death.

5out of 5Elentarri–"The Art of Statistics" provides a brief and not terribly technical guide to the essential statistical principles required to obtain knowledge from data, all while using real world problems as examples to introduce conceptual issues and analytical techniques. Spiegelhalter also discusses the which techniques to use and why and the ethics of reporting distorted and inaccurate headline-catching data. He uses both good and bad examples of statistical reporting and explains why they are god/bad. As "The Art of Statistics" provides a brief and not terribly technical guide to the essential statistical principles required to obtain knowledge from data, all while using real world problems as examples to introduce conceptual issues and analytical techniques. Spiegelhalter also discusses the which techniques to use and why and the ethics of reporting distorted and inaccurate headline-catching data. He uses both good and bad examples of statistical reporting and explains why they are god/bad. As we are assailed with more statistical data, it is important for us to become acquainted and thus literate in the methods of data manipulation and what the results ultimately mean, so we can properly assess the the findings and be more informed about the statistics they encounter. This isn't a statistics text book. If you need in depth instructions on the various methods of data analysis, a proper statistics text book and the relevant analytics software would be more useful. This is an introductory text for the general interested public and first year college/university students. The book does however, provide some eye opening information about statistical reporting and the various methods used to obtain that data. Interesting and informative.

4out of 5Nick Davies–I can't remember where I read reviews of this being very good, but they were right. Here Spiegelhalter ("Spiegelhalter, Spiegelhalter an der Wand, wer ist die Schönste im ganzen Land?") attempts to explain the uses and abuses of statistics and probability. It's well put together, well explained, well illustrated. The pace is good, the examples well-chosen. I can't really complain, and I'm only really giving it a harsh four stars because.. well.. it's a book about statistics. A very good one, an I can't remember where I read reviews of this being very good, but they were right. Here Spiegelhalter ("Spiegelhalter, Spiegelhalter an der Wand, wer ist die Schönste im ganzen Land?") attempts to explain the uses and abuses of statistics and probability. It's well put together, well explained, well illustrated. The pace is good, the examples well-chosen. I can't really complain, and I'm only really giving it a harsh four stars because.. well.. it's a book about statistics. A very good one, an informative one, but dry in truth - quirky bits in necessary moderation.

4out of 5Oscar Despard–This volume is a worthy, pacy introduction to statistics. Spiegelhalter is an engaging writer; he peppers his explanations of core concepts with pleasing anecdotes, exemplifying the principles of honest, yet exciting storytelling from data that he propounds. He explains clearly, and from from the most basic ideas, elements of statistics that can be obscured by a risk to apply them mathematically, and is honest about the disconcerting complexity of its underlying concepts. My only wish is for a s This volume is a worthy, pacy introduction to statistics. Spiegelhalter is an engaging writer; he peppers his explanations of core concepts with pleasing anecdotes, exemplifying the principles of honest, yet exciting storytelling from data that he propounds. He explains clearly, and from from the most basic ideas, elements of statistics that can be obscured by a risk to apply them mathematically, and is honest about the disconcerting complexity of its underlying concepts. My only wish is for a slightly more mathematical treatment of some of the topics covered, but that is in a way a consequence of the book’s virtues: it leaves the reader desperate to know more about this fascinating branch of mathematics than would be possible in this book. This book clarified many of the half-learnt statistical techniques of which I was aware, and I could not recommend it more to anyone interested in modern scientific practice.

4out of 5John–This was exceptional! If you have ever wanted to learn more about the ubiquitous statistics that are a part of our lives, but worried you were going to end up reading a mathematically laden statistics text, this is the book I'd recommend! Yes, there are some charts, graphs, and a few equations (mostly in the glossary). However, Spiegelhalter does a great job getting into the basics and provides much help in deciphering the "how did they come up with that?" that we all experience when reading an a This was exceptional! If you have ever wanted to learn more about the ubiquitous statistics that are a part of our lives, but worried you were going to end up reading a mathematically laden statistics text, this is the book I'd recommend! Yes, there are some charts, graphs, and a few equations (mostly in the glossary). However, Spiegelhalter does a great job getting into the basics and provides much help in deciphering the "how did they come up with that?" that we all experience when reading an article or book that quotes studies that don't seem to make logical sense. He starts with data and how data can be used to draw conclusions. Then builds on measurement, what causes what, and modeling. These are the basics of much published information we read, hear, or get bombarded with on a daily basis. Is it accurate? Is it reliable? Are the conclusions unbiased? We all have thought these things. From here, he continues to regression, estimates, and probability. He is able to do this all with out bogging down into all the actual summation and stochastics that normally accompanies any discussion about statistics. It is refreshing and sets the divide of statistics for statisticians and statistics for average people. For me, the true gem of the book is the end. Here he gives us ammunition to decipher claims or "discoveries" that may not be fully accurate. He discussed a list of items to ensure quality and ethical honesty in the data, compilation, dissemination and use of any study. Further, he discusses stories where things to go terribly wrong, not from malice, but from ignorance. I have seen other reviews by actually statisticians and actuaries and they also think this is a fabulous book. I hope it goes mainstream and is required reading for any journalist and editors prior to their first articles written, edited, or approved.

5out of 5Roozbeh Daneshvar–I did need this book and I wish I had read it much earlier. It goes over the basics of statistics and the mistakes one might make, all in an easy, straightforward and amusing narration. It also has plenty of good examples. I liked the subtle humor (maybe a British one?) and I was a bit disappointed with the numerous errors and typos (I gelt that it was published in a haste). If you deal with data and statistics, either professionally or even if in the daily and mundane level, you might find this b I did need this book and I wish I had read it much earlier. It goes over the basics of statistics and the mistakes one might make, all in an easy, straightforward and amusing narration. It also has plenty of good examples. I liked the subtle humor (maybe a British one?) and I was a bit disappointed with the numerous errors and typos (I gelt that it was published in a haste). If you deal with data and statistics, either professionally or even if in the daily and mundane level, you might find this book a good and useful read. Also if you teach statistics, data or other similar concepts, this book might be a good choice. Below I am bringing one sample quote from this book: It is not just scientists who value discoveries - the delight in finding something new is universal. In fact it is so desirable that there is an innate tendency to feel we have found something when we have not. We previously used the term apophenia to describe the capacity to see patterns where they do not exist, and it has been suggested that this tendency might even confer an evolutionary advantage - those ancestors who ran away from rustling in the bushes without waiting to find out whether it was definitely a tiger may have been more likely to survive. But while this attitude may be fine for hunter-gatherers, it cannot work in science - indeed, the whole scientific process is undermined if claims are just figments of our imagination. There must be a way of protecting us against false discoveries, and hypothesis testing attempts to fill that role.

4out of 5Anastasia–This book was just okay - I can't help but feel that if Spiegelhalter did one of the things he wanted to accomplish in this book it would have been great, but he tried to make this book all things to all people and it ended up being too shallow on both fronts. I'm beating around the bush a bit but essentially Spiegelhalter wanted to 1) teach the audience about statistics and how they can make life better and 2) present some cool scenarios where statistics can get us an approximate answer to some This book was just okay - I can't help but feel that if Spiegelhalter did one of the things he wanted to accomplish in this book it would have been great, but he tried to make this book all things to all people and it ended up being too shallow on both fronts. I'm beating around the bush a bit but essentially Spiegelhalter wanted to 1) teach the audience about statistics and how they can make life better and 2) present some cool scenarios where statistics can get us an approximate answer to something - like how likely someone would have been to survive the Titanic, if ovarian cancer screening is good, whether busier hospitals have higher survival rates, and so on. I found that Spiegelhalter had sections that were conversational and easy to read, and then I got whiplash going into other sections that were incredibly dense and requiring intense engagement from the reader. Ultimately it made it difficult to determine the context in which to read this - was it a casual commute read or something that I want to make sure I had a pen and paper ready to take notes for. If you're looking for a good foundational stats book, I would recommend picking up Charles Wheelan's Naked Statistics rather than this one.

5out of 5Tony Fitzpatrick–Sir David Spiegelhalter is a noted Professor and internationally recognized authority on statistics and risk - part of his remit is "the public understanding of risk". This book attempts to explain the basis of statistical theory and equip the reader to understand concepts such as confidence intervals, legally permissible statistical evidence, and some of the deceptions used by journalists (and academics) to "big up" their findings. The narrative is very good, and enlightening. The maths is howe Sir David Spiegelhalter is a noted Professor and internationally recognized authority on statistics and risk - part of his remit is "the public understanding of risk". This book attempts to explain the basis of statistical theory and equip the reader to understand concepts such as confidence intervals, legally permissible statistical evidence, and some of the deceptions used by journalists (and academics) to "big up" their findings. The narrative is very good, and enlightening. The maths is however difficult, and not well explained - at several points Sir David tries to explain a concept but then gives up mid-explanation, having decided it is maybe too hard to convey in a book of this type. The text therefore only partially succeeded in its mission to explain to the layman some very important and powerful concepts. As Spiegelhalter says when discussing probability, "even statisticians of many years standing find the subject hard and non-intuitive"!

4out of 5Daniel Wrench–Excellent. Entertaining and insightful, even (or especially) for someone who has just finished their degree in statistics and is looking to make it their career. Great examples and explanation of how we can trip up when it comes to stats and probability.

4out of 5Jack–This book had some interesting thoughts and did a pretty good job of explaining in non mathematical terms how statistics work. I personally think it’s easier to explain if you include the math, but hey that’s just my bias. When I read books like this I’m reminded of how the media, the government and I guess everybody else distorts what the statistics are really saying. I’m beginning to believe we should just let real statisticians tell us what the data mean and what they don’t mean . If they cou This book had some interesting thoughts and did a pretty good job of explaining in non mathematical terms how statistics work. I personally think it’s easier to explain if you include the math, but hey that’s just my bias. When I read books like this I’m reminded of how the media, the government and I guess everybody else distorts what the statistics are really saying. I’m beginning to believe we should just let real statisticians tell us what the data mean and what they don’t mean . If they could agree on the meaning among themselves. This convinced me to just stop listening to any statistics the media puts out there

4out of 5Jo Bennie–Written by an experienced forensic statitician this is a well-informed insight into the use and, often, sadly, misuse of statistics that is generated from the data surrounding us. It gives a good grounding in the correlation vs causation, algorithms, probability and critical thinking that gives the lay reader the tools to assess and appraise the figures shouted at us in the news, on social media, in government decision-making

5out of 5Rob–This is an undeniably interesting book and there werechunks that I thoroughly enjoyed, but as someone not blessed with most statistical brain, I found swathes of it largely unintelligible. For the right person, the entire book will be manna from heaven. That person is - sadly - not me.

4out of 5Alexandros Potapidis–David Spiegelhalter demonstrates, in the most simple and comical manner, how data and statistics can be a force for good or ill. His examples, ranging from data of heart surgery of little children and the association of bacon in increasing the chances of having cancer, to the probability of certain Titanic passengers having more chances of survival than others, or how parents' hight affects the hight of their children. The author takes us step by step to reveal how statistics and data can be man David Spiegelhalter demonstrates, in the most simple and comical manner, how data and statistics can be a force for good or ill. His examples, ranging from data of heart surgery of little children and the association of bacon in increasing the chances of having cancer, to the probability of certain Titanic passengers having more chances of survival than others, or how parents' hight affects the hight of their children. The author takes us step by step to reveal how statistics and data can be manipulated by practitioners, press releases, communicators, and the press. Significantly, he illustrates how certain mathematical and statistical models can influence the outcomes. There is a prevalent perception that there is such thing as objective data, that numbers never lie. This book comes to undermine this popular perception and demonstrate how scientists are using data to tell a certain story, they "make comparisons and judgements", no matter how much they want to show that they are trying to "inform and not persuade", (p. 69). For example, in one statistical school of thought (Bayesian Approach), there is an explicit pronouncement of subjectivity, where probabilities depend on our relationship with the outside world and "data does not speak for itself" (p. 307). Beyond the scientist's unconscious bias, there is also the inherent limitation of data when it comes to measuring. Data is "always an imperfect measure of what we are really interested in" (p. 10), because of variability and incompatibility of the subjects, time distance, and geography. If we add to this the divaded statistical community, that can not produce a single unifying theory of statistical inference (p. 305), and some seriously questionable research practices by some members of the scientific community (p. 350), one would think that statistics look more like a social science. But applying data in the right context, with the right tools, and the best intentions, can produce some remarkable stories that will shade some light to our social world and make predictions that will improve our lives. In this respect, Spiegelhalter offers PPDAC model of statistical research. This consists of identifying the Problem in hand, such as the increasing rate of teenage pregnancy for example. Subsequently comes the Plan of the methodological approach, followed by the accumulation of Data. The data is then Analysed, classified, and put into perspective to produce the Conclusion. Consequently, this will create more questions that will require further exploring, and the circle will start again. Overall, statistical analysis, when done the right way, can produce remarkable results and improve our world. The Art of Statistics demonstrates that we, as an audience and data receivers, should be worry and critical when impressive pronouncements are made. Some basic education is needed to achieve this, and this books is a start towards this direction.

5out of 5Kade Flowers–As somebody who is massively into statistics, and reading different interpretations on teaching statistical concepts, this book was a beautiful and welcome breath of fresh air. It starts of lightly by exploring different methods of reviewing statistical data, and visualising and summarising their core messages using graphs and tables respectively, and then progresses on to more difficult to grasp concepts such as algorithms and probability theory. This book is excellent for people who are scient As somebody who is massively into statistics, and reading different interpretations on teaching statistical concepts, this book was a beautiful and welcome breath of fresh air. It starts of lightly by exploring different methods of reviewing statistical data, and visualising and summarising their core messages using graphs and tables respectively, and then progresses on to more difficult to grasp concepts such as algorithms and probability theory. This book is excellent for people who are scientists and deal with complex statistics often as well as the general public who are exposed to questionable statistical interpretations from the media - and the tone of language is, in my opinion, cleverly moulded to encompass these different readers alike. I will say however, this book is probably not the best statistical book to start reading if you are not already familiar with the core concepts of statistics such as P-Values, standard deviations and measurements of uncertainty. Although Spiegelhalter does explain these concepts before delving into more detail, I am sure (and can tell) that he did not want to spend too much time trying to wrap the readers head around these concepts from scratch. Even as someone very familiar with statistics (I am a scientist my self), I found my self having to re-read some chapters in order to fully understand them. I do not believe that's because they're poorly explained (I think they're explained very well), but simply because they're more advanced than I'm used to. That's exactly why, however, this book is refreshing - as it is nice to not be almost back at GCSE level when trying to get new insights into statistical thinking and applications. I thoroughly recommend 'Statistics Without Tears' by Derek Rowntree (a short read but an excellent introduction to concepts) before reading this book to get the most out of it.

4out of 5Tomas Liutvinas–Wouldn't even come to mind to seek out and read a book like this (Got it as a birthday present). And I've gotta say that it was really cool since it had the two properties that I like the most in the book. It was practical knowledge (non-fiction) and changed the way I experience the world (expanded my perspective when it comes to various scientific studies and conclusions drawn from statistics). It wasn't perfectly chaptered to my liking as I had to stop mid-chapter sometimes, but still, it was re Wouldn't even come to mind to seek out and read a book like this (Got it as a birthday present). And I've gotta say that it was really cool since it had the two properties that I like the most in the book. It was practical knowledge (non-fiction) and changed the way I experience the world (expanded my perspective when it comes to various scientific studies and conclusions drawn from statistics). It wasn't perfectly chaptered to my liking as I had to stop mid-chapter sometimes, but still, it was reasonably fun and interesting to read. I wouldn't put this down as a book that you can quickly pick up and put down, I'd suggest reading this in more focused sessions when you've got a bit more than 15 or 20 minutes to spare. It explains the things to look out for or pay extra attention to when consuming mainstream media and all of the reports claiming all sorts of findings. Probably less relatable (but still interesting!) bits about how some seemingly minor actions or decisions during research execution can affect research results. There is much much more than that in this book, it's not limited to research stuff, also does a good job in giving many samples and many new perspectives and entertaining/interesting examples of various types of statistics. And everything from real-world cases, which is really cool. 😄 It does include some math formulas, statistics terms, explained in a very nice way in the glossary provided at the end of the book. This way math people can dig into that and pay more attention, whilst all the sane people can just read the logical meaning behind them (still at the glossary). Yep, something different, and yet still relevant and really interesting. Would recommend for people who like reading a lot of research papers or for people who believe in every mainstream media published article ever 😄 and for everyone else, really I think this should be a must-read for every bachelor's degree student in every path of academics involved with logic and reasoning.