Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis

Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis

by Ethan Bueno de Mesquita, Anthony Fowler
Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis

Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis

by Ethan Bueno de Mesquita, Anthony Fowler

Paperback

$31.95 
  • SHIP THIS ITEM
    Qualifies for Free Shipping
  • PICK UP IN STORE
    Check Availability at Nearby Stores

Related collections and offers


Overview

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 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 real-world examples and data from a wide variety of subjects
  • Includes practice questions and data exercises

Product Details

ISBN-13: 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

Ethan Bueno de Mesquita is the Sydney Stein Professor and deputy dean at the Harris School of Public Policy at the University of Chicago. He is the author of Political Economy for Public Policy and the coauthor of Theory and Credibility: Integrating Theoretical and Empirical Social Science (both Princeton). Twitter @ethanbdm Anthony Fowler is a professor at the Harris School of Public Policy at the University of Chicago.

Table of Contents

Preface xvii

Organization xviii

Who Is This Book For? xix

Acknowledgments xx

Chapter 1 Thinking Clearly in a Data-Driven Age 1

What You'll Learn 1

Introduction 1

Cautionary Tales 2

Abe's hasty diagnosis 2

Civil resistance 3

Broken-windows 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 10,000-hour 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

Post-mortems 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, Non-Linear 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 Over-Comparing, Under-Reporting 113

What You'll Learn 113

Introduction 113

Can an octopus be a soccer expert? 113

Publication Bias 118

p-hacking 119

p-screening 120

Are Most Scientific "Facts" False? 122

ESP 122

Get out the vote 123

p-hacking forensics 124

Potential Solutions 126

Reduce the significance threshold 126

Adjust p-values for multiple testing 127

Don't obsess over statistical significance 127

Pre-registration 127

Requiring pre-registration 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 10,000-hour 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 Difference-in-Differences 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 gender-wage gap 276

Useful Diagnostics 278

Do newspaper endorsements affect voting decisions? 278

Is obesity contagious? 279

Difference-in-Differences 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 at-risk 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

Miles-per-gallon versus gallons-per-mile 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

Ill-defined 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

Cost-benefit 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

From the Publisher

“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 chock-full 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 data-rich twenty-first 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 well-trained 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 must-read for anyone looking to be smarter in our data-driven world.”—Andrea Jones-Rooy, New York University

From the B&N Reads Blog

Customer Reviews