Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis
400Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis
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Overview
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 decisionmaking; and how to make better decisions by using moral values as well as data. Filled with realworld 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 datadriven age, data can never be a substitute for thinking.
 An ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fields
 Introduces the basic toolkit of data analysis—including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity
 Uses realworld examples and data from a wide variety of subjects
 Includes practice questions and data exercises
Product Details
ISBN13:  9780691214351 

Publisher:  Princeton University Press 
Publication date:  11/16/2021 
Pages:  400 
Sales rank:  631,504 
Product dimensions:  7.00(w) x 10.00(h) x (d) 
About the Author
Table of Contents
Preface xvii
Organization xviii
Who Is This Book For? xix
Acknowledgments xx
Chapter 1 Thinking Clearly in a DataDriven Age 1
What You'll Learn 1
Introduction 1
Cautionary Tales 2
Abe's hasty diagnosis 2
Civil resistance 3
Brokenwindows policing 5
Thinking and Data Are Complements, Not Substitutes 7
Readings and References 9
Part I Establishing a Common Language 11
Chapter 2 Correlation: What Is It and What Is It Good For? 13
What You'll Learn 13
Introduction 13
What Is a Correlation? 13
Fact or correlation? 18
What Is a Correlation Good For? 19
Description 19
Forecasting 20
Causal inference 23
Measuring Correlations 24
Mean, variance, and standard deviation 24
Covariance 27
Correlation coefficient 28
Slope of the regression line 29
Populations and samples 29
Straight Talk about Linearity 30
Wrapping Up 33
Key Terms 33
Exercises 34
Readings and References 36
Chapter 3 Causation: What Is It and What Is It Good For? 37
What You'll Learn 37
Introduction 37
What Is Causation? 38
Potential Outcomes and Counterfactuals 39
What Is Causation Good For? 40
The Fundamental Problem of Causal Inference 41
Conceptual Issues 42
What is the cause? 42
Causality and counterexamples 44
Causality and the law 47
Can causality run backward in time? 47
Does causality require a physical connection? 48
Causation need not imply correlation 49
Wrapping Up 49
Key Terms 50
Exercises 50
Readings and References 52
Part II Does a Relationship Exist? 53
Chapter 4 Correlation Requires Variation 55
What You'll Learn 55
Introduction 55
Selecting on the Dependent Variable 56
The 10,000hour rule 57
Corrupting the youth 59
High school dropouts 62
Suicide attacks 63
The World Is Organized to Make Us Select on the Dependent Variable 64
Doctors mostly see sick people 65
Postmortems 65
The Challenger disaster 67
The financial crisis of 2008 69
Life advice 69
Wrapping Up 70
Key Term 70
Exercises 70
Readings and References 72
Chapter 5 Regression for Describing and Forecasting 74
What You'll Learn 74
Introduction 74
Regression Basics 74
Linear Regression, NonLinear Data 79
The Problem of Overfitting 87
Forecasting presidential elections 87
How Regression Is Presented 89
A Brief Intellectual History of Regression 89
Wrapping Up 91
Key Terms 91
Exercises 92
Readings and References 93
Chapter 6 Samples, Uncertainty, and Statistical Inference 94
What You'll Learn 94
Introduction 94
Estimation 94
Why Do Estimates Differ from Estimands? 96
Bias 96
Noise 97
What Makes for a Good Estimator? 98
Quantifying Precision 99
Standard errors 99
Small samples and extreme observations 101
Confidence intervals 102
Statistical Inference and Hypothesis Testing 103
Hypothesis testing 103
Statistical significance 104
Statistical Inference about Relationships 105
What If We Have Data for the Whole Population? 106
Substantive versus Statistical Significance 107
Social media and voting 107
The Second Reform Act 108
Wrapping Up 109
Key Terms 109
Exercises 110
Readings and References 111
Chapter 7 OverComparing, UnderReporting 113
What You'll Learn 113
Introduction 113
Can an octopus be a soccer expert? 113
Publication Bias 118
phacking 119
pscreening 120
Are Most Scientific "Facts" False? 122
ESP 122
Get out the vote 123
phacking forensics 124
Potential Solutions 126
Reduce the significance threshold 126
Adjust pvalues for multiple testing 127
Don't obsess over statistical significance 127
Preregistration 127
Requiring preregistration in drug trials 128
Replication 128
Football and elections 129
Test important and plausible hypotheses 130
The power pose 131
Beyond Science 131
Superstars 132
Wrapping Up 134
Key Terms 134
Exercises 134
Readings and References 136
Chapter 8 Reversion to the Mean 138
What You'll Learn 138
Introduction 138
Does the truth wear off? 138
Francis Galton and Regression to Mediocrity 139
Reversion to the Mean Is Not a Gravitational Force 142
Seeking Help 145
Does knee surgery work? 146
Reversion to the Mean, the Placebo Effect, and Cosmic Habituation 147
The placebo effect 147
Cosmic habituation explained 148
Cosmic habituation and genetics 150
Beliefs Don't Revert to the Mean 150
Wrapping Up 152
Key Words 152
Exercises 152
Readings and References 155
Part III Is the Relationship Causal? 157
Chapter 9 Why Correlation Doesn't Imply Causation 159
What You'll Learn 159
Introduction 159
Charter schools 160
Thinking Clearly about Potential Outcomes 163
Sources of Bias 168
Confounders 168
Reverse causality 169
The 10,000hour rule, revisited 170
Diet soda 173
How Different Are Confounders and Reverse Causality? 174
Campaign spending 174
Signing the Bias 176
Contraception and HIV 179
Mechanisms versus Confounders 181
Thinking Clearly about Bias and Noise 183
Wrapping Up 186
Key Terms 187
Exercises 187
Readings and References 191
Chapter 10 Controlling for Confounders 193
What You'll Learn 193
Introduction 193
Party whipping in Congress 193
A note on heterogeneous treatment effects 197
The Anatomy of a Regression 198
How Does Regression Control? 201
Controlling and Causation 209
Is social media bad for you? 210
Reading a Regression Table 211
Controlling for Confounders versus Mechanisms 213
There Is No Magic 214
Wrapping Up 215
Key Terms 215
Exercises 216
Readings and References 217
Chapter 11 Randomized Experiments 218
What You'll Learn 218
Introduction 218
Breastfeeding 219
Randomization and Causal Inference 221
Estimation and Inference in Experiments 224
Standard errors 224
Hypothesis testing 225
Problems That Can Arise with Experiments 225
Noncompliance and instrumental variables 226
Chance imbalance 232
Lack of statistical power 234
Attrition 235
Interference 236
Natural Experiments 237
Military service and future earnings 238
Wrapping Up 239
Key Terms 239
Exercises 240
Readings and References 242
Chapter 12 Regression Discontinuity Designs 243
What You'll Learn 243
Introduction 243
How to Implement an RD Design 247
Are extremists or moderates more electable? 249
Continuity at the Threshold 251
Does continuity hold in election RD designs? 255
Noncompliance and the Fuzzy RD 256
Bombing in Vietnam 257
Motivation and Success 261
Wrapping Up 262
Key Terms 262
Exercises 262
Readings and References 264
Chapter 13 DifferenceinDifferences Designs 266
What You'll Learn 266
Introduction 266
Parallel Trends 267
Two Units and Two Periods 269
Unemployment and the minimum wage 269
N Units and Two Periods 272
Is watching TV bad for kids? 273
N Units and N Periods 275
Contraception and the genderwage gap 276
Useful Diagnostics 278
Do newspaper endorsements affect voting decisions? 278
Is obesity contagious? 279
DifferenceinDifferences as Gut Check 282
The democratic peace 282
Wrapping Up 285
Key Terms 285
Exercises 286
Readings and References 288
Chapter 14 Assessing Mechanisms 290
What You'll Learn 290
Introduction 290
Causal Mediation Analysis 291
Intermediate Outcomes 292
Cognitive behavioral therapy and atrisk youths in Liberia 293
Independent Theoretical Predictions 294
Do voters discriminate against women? 294
Testing Mechanisms by Design 295
Social pressure and voting 295
Disentangling Mechanisms 296
Commodity price shocks and violent conflict 296
Wrapping Up 298
Key Terms 299
Exercises 299
Readings and References 300
Part IV From Information to Decisions 303
Chapter 15 Turn Statistics into Substance 305
What You'll Learn 305
Introduction 305
What's the Right Scale? 305
Milespergallon versus gallonspermile 306
Percent versus percentage point 309
Visual Presentations of Data 309
Policy preferences and the Southern realignment 311
Some rules of thumb for data visualization 314
From Statistics to Beliefs: Bayes' Rule 314
Bayes' rule 317
Information, beliefs, priors, and posteriors 318
Abe's celiac revisited 319
Finding terrorists in an airport 322
Bayes' rule and quantitative analysis 325
Expected Costs and Benefits 328
Screening frequently or accurately 329
Wrapping Up 331
Key Words 331
Exercises 332
Readings and References 334
Chapter 16 Measure Your Mission 336
What You'll Learn 336
Introduction 336
Measuring the Wrong Outcome or Treatment 337
Partial measures 337
Metal detectors in airports 337
Intermediate outcomes 339
Blood pressure and heart attacks 340
Illdefined missions 341
Climate change and economic productivity 342
Do You Have the Right Sample? 343
External validity 343
Malnutrition in India and Bangladesh 343
Selected samples 344
College admissions 345
Why can't major league pitchers hit? 345
Strategic Adaptation and Changing Relationships 349
The duty on lights and windows 349
The shift in baseball 350
The war on drugs 351
Wrapping Up 353
Key Words 353
Exercises 353
Readings and References 355
Chapter 17 On the Limits of Quantification 357
What You'll Learn 357
Introduction 357
Decisions When Evidence Is Limited 358
Costbenefit analysis and environmental regulation 358
Floss your teeth and wear a mask 359
Floss your teeth 359
Wear a mask 360
Quantification and Values 361
How quantitative tools sneak in values 361
Algorithms and racial bias in health care 361
How quantification shapes our values 363
Think Clearly and Help Others Do So Too 367
Exercises 367
Readings and References 368
Index 371
What People are Saying About This
“A common phrase one hears in public life is that correlations and causality are the same but different. But how are they the same and how exactly do they differ? Thinking Clearly with Data threads a needle between two advanced subjects by clearly laying out a theory of both. This book is destined to become a classic and, if we are lucky, will be on every social scientist’s shelf.”—Scott Cunningham, Baylor University
“Witty, erudite, and chockfull of memorable and engaging examples, Thinking Clearly with Data brings core statistical ideas to life. The insights it offers are helpful not only to scholars in search of creative research strategies but also to readers who are simply trying to make sensible everyday decisions on topics from parenting to personal finance.”—Donald P. Green, Columbia University
“By making thinking the primary focus in teaching data analysis, Thinking Clearly with Data fills a big need.”—Dustin Tingley, Harvard University
“Whether you are a social scientist engaged in research, an attorney pleading a case, or a patient deciding on a medical treatment, you need to read Thinking Clearly with Data. This timely—and useful—book for making decisions in the datarich twentyfirst century is one that everyone who thinks about evidence should read.”—Lynn Vavreck, University of California, Los Angeles
“Thinking Clearly with Data gives readers the necessary tools to be critical consumers of claims that others make based on data, and even to start making credible claims based on data themselves.”—Andy Eggers, University of Chicago
“Rather than getting bogged down in the math and statistics underlying the methods, Thinking Clearly with Data walks students through the big ideas of what can be learned from data and flags common mistakes even welltrained data analysts make.”—Jonathan Davis, University of Oregon
“Thinking Clearly with Data is one of the most accessible and welcoming books I’ve seen on how to make sense of the world with data, thoughtfulness, and rigor. It’s a mustread for anyone looking to be smarter in our datadriven world.”—Andrea JonesRooy, New York University