Includes index.

Preface -
CHAPTER 1 Thinking Clearly in a Data-Driven Age -
PART I ESTABLISHING A COMMON LANGUAGE:
CHAPTER 2 Correlation: What Is It and What Is It Good For? -
CHAPTER 3 Causation: What Is It and What Is It Good For? -
PART II DOES A RELATIONSHIP EXIST?:
CHAPTER 4 Correlation Requires Variation -
CHAPTER 5 Regression for Describing and Forecasting -
CHAPTER 6 Samples, Uncertainty, and Statistical Inference -
CHAPTER 7 Over-Comparing, Under-Reporting -
CHAPTER 8 Reversion to the Mean -
PART III IS THE RELATIONSHIP CAUSAL?:
CHAPTER 9 Why Correlation Doesn’t Imply Causation -
CHAPTER 10 Controlling for Confounders -
CHAPTER 11 Randomized Experiments -
CHAPTER 12 Regression Discontinuity Designs -
CHAPTER 13 Difference-in-Differences Designs -
CHAPTER 14 Assessing Mechanisms -
PART IV FROM INFORMATION TO DECISIONS:
CHAPTER 15 Turn Statistics into Substance -
CHAPTER 16 Measure Your Mission -
CHAPTER 17 On the Limits of Quantification -
Index.

"This is an intro-level text that teaches how to think clearly and conceptually about quantitative information, emphasizing ideas over technicality and assuming no prior exposure to data analysis, statistics, or quantitative methods. The books four parts present the foundation for quantiative reasoning: correlation and causation; statistical relationships; causal phenomena; and incorporating quantitative information into decision making. Within these parts it covers the array of tools used by social scientists, including regression, inference, experiments, research design, and more, all by explaining the rationale and logic behind such tools rather than focusing only on the technical calculations used for each. New concepts are presented simply, with the help of copious examples, and the books leans towards graphic rather than mathematical representation of data, with any technical material included in appendices"-- Provided by publisher.