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Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis
An engaging introduction to data science that emphasizes critical thinking over statistical techniques
An introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives.
Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data. Filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel.
Above all else, Thinking Clearly with Data demonstrates why, despite the many benefits of our data-driven age, data can never be a substitute for thinking.
An introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives.
Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data. Filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel.
Above all else, Thinking Clearly with Data demonstrates why, despite the many benefits of our data-driven age, data can never be a substitute for thinking.
400 pages, Hardcover
Published November 16, 2021
78 people are currently reading
384 people want to read
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Displaying 1 - 6 of 6 reviews
June 27, 2023
Hello and welcome back to the series I am (not so briefly) titling "Rating my college textbooks because if I have to read them, they better count toward my Goodreads challenge!" And today, we've got an exciting one - my introductory data science textbook! As always, rating on a curve (although this book hardly needs it!).
In all honesty, I had to skim through the blurb for this book just to make sure it is in fact considered a textbook, because reading it felt like an experience all its own! Now, take this with a grain of salt because I am a data science major (so I know the importance of accounting for bias!), but I really did find this book super enjoyable. The key concepts were all super relevant and clearly expressed. The examples made the ideas easier to understand but also really helped emphasize their importance, and I found myself super engrossed when reading them. (I'm still thinking about the charter schools example on at least a weekly basis!) When I was reading, I wanted to grab the person next to me and be like, look at this!! (Fortunately for the sake of all my fellow students in the library, I restrained myself, but the book needed to be appreciated!)
Like I said, I'm definitely a special case because this topic is my thing, but I think even those who don't have data science/statistics as a main field of study could appreciate and learn from this book. The formulas were certainly overwhelming at times, but other than that, it felt as approachable as textbooks go - it was definitely a great fit for an introduction to data science class. As an added plus, it was cheap to buy a print version on Amazon, so I still have it with me - I wouldn't be surprised if I go back and read some of the chapters my class skipped over in my free time!
In all honesty, I had to skim through the blurb for this book just to make sure it is in fact considered a textbook, because reading it felt like an experience all its own! Now, take this with a grain of salt because I am a data science major (so I know the importance of accounting for bias!), but I really did find this book super enjoyable. The key concepts were all super relevant and clearly expressed. The examples made the ideas easier to understand but also really helped emphasize their importance, and I found myself super engrossed when reading them. (I'm still thinking about the charter schools example on at least a weekly basis!) When I was reading, I wanted to grab the person next to me and be like, look at this!! (Fortunately for the sake of all my fellow students in the library, I restrained myself, but the book needed to be appreciated!)
Like I said, I'm definitely a special case because this topic is my thing, but I think even those who don't have data science/statistics as a main field of study could appreciate and learn from this book. The formulas were certainly overwhelming at times, but other than that, it felt as approachable as textbooks go - it was definitely a great fit for an introduction to data science class. As an added plus, it was cheap to buy a print version on Amazon, so I still have it with me - I wouldn't be surprised if I go back and read some of the chapters my class skipped over in my free time!
November 19, 2022
I've embarked on a graduate school journey to get an MPP, and this is an introductory textbook. It's written in super accessible language for those who do not have a background or undergraduate education in quantitative fields and analysis, and strikes a great balance between explanatory examples and wordy tone. It's gotten me through my first quarter and definitely a great foundation for exploring statistics, regressions, and data science without frightening the layman or laywoman away from it - it really does emphasize the critical thinking and encourages you to engage your brain to work through it rather than toss numbers at you. I can tell you, from a non-quantitative undergraduate student, the latter makes us freeze! It's solid enough that I'd like a physical copy in addition to the eBook copy I've been using for class, because the principles are probably useful to remember as you progress into higher and more complicated levels of study. I highly recommend this!
February 22, 2022
Good intention but too qualitative, inapplicable, and verbose.
April 19, 2023
Before highlighting the benefits, it's important to point out what this book is NOT:
1) An in-depth handbook on mathematics and statistics: Concepts are carefully explained, but learners will not be bombarded with complex jargon and formulae. There is strong emphasis on linear regression, and the questions in the exercises do not involve heavy calculation.
2) A guide on computational techniques: No advanced software or programming language is required. Most of the problems can be easily tackled with pen and paper or a low level spreadsheet tool.
With those aside, here's why this text is arguably a MUST for all data students and professionals:
1) It is very detailed with the basics, and covers plenty of ground on the strengths and limitations of regression modelling, including crucial but often overlooked issues such as counterfactuals, p-hacking/screening and confounding variables.
2) There are numerous examples and case studies across a range of disciplines, and the authors spend much effort showing us the ways in which quantitative findings can be interpreted in different contexts.
3) It adopts a highly instrumental approach to the field, and eschews any excess of numerical information and visualisation. The key takeaway is that quantitative analysis is a means to an end, so one must be clear about the objectives, and complement the tool(s) with critical thinking and robust research designs.
1) An in-depth handbook on mathematics and statistics: Concepts are carefully explained, but learners will not be bombarded with complex jargon and formulae. There is strong emphasis on linear regression, and the questions in the exercises do not involve heavy calculation.
2) A guide on computational techniques: No advanced software or programming language is required. Most of the problems can be easily tackled with pen and paper or a low level spreadsheet tool.
With those aside, here's why this text is arguably a MUST for all data students and professionals:
1) It is very detailed with the basics, and covers plenty of ground on the strengths and limitations of regression modelling, including crucial but often overlooked issues such as counterfactuals, p-hacking/screening and confounding variables.
2) There are numerous examples and case studies across a range of disciplines, and the authors spend much effort showing us the ways in which quantitative findings can be interpreted in different contexts.
3) It adopts a highly instrumental approach to the field, and eschews any excess of numerical information and visualisation. The key takeaway is that quantitative analysis is a means to an end, so one must be clear about the objectives, and complement the tool(s) with critical thinking and robust research designs.
July 13, 2023
Ethan Bueno De Mesquita has a true talent for making technical social science stuff seem accessible and important (Political Economy For Public Policy is his other textbook). This new book is no exception.
As he argues on his Twitter:
“We believe that thinking clearly about quantitative evidence is a prerequisite for being an informed human being and citizen. It can no longer be the purview solely of those with a knack for the technical.”
He has me convinced. Frankly, everyone ought to read this book. Our world is overrun by unclear thinking. This is a great first step.
**Also, this is incredibly useful reference for anyone who needs an easy & robust intro or refresher on statistics/data-science.
As he argues on his Twitter:
“We believe that thinking clearly about quantitative evidence is a prerequisite for being an informed human being and citizen. It can no longer be the purview solely of those with a knack for the technical.”
He has me convinced. Frankly, everyone ought to read this book. Our world is overrun by unclear thinking. This is a great first step.
**Also, this is incredibly useful reference for anyone who needs an easy & robust intro or refresher on statistics/data-science.
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March 9, 2025finally finished and i am so sleepy.
Displaying 1 - 6 of 6 reviews