PREFACE Fun chapter titles The chapter titles in the book are descriptive. Here are more dramatic titles intended to evoke some of the surprise you should feel when working through this material:

Part 1:
- Chapter 1: Prediction as a unifying theme in statistics and causal inference.
- Chapter 2: Data collection and visualization are important.?
- Chapter 3: Here's the math you actually need to know.
- Chapter 4: Time to unlearn what you thought you knew about statistics.
- Chapter 5: You don't understand your model until you can simulate from it.
Part 2:
- Chapter 6: Let's think deeply about regression.
- Chapter 7: You can't t just do regression, you have to understand regression.
- Chapter 8: Least squares and all that.
- Chapter 9: Let's be clear about our uncertainty and about our prior knowledge. - Chapter 10: You don't just fit models, you build models.
- Chapter 11: Can you convince me to trust your model?
- Chapter 12: Only fools work on the raw scale.
Part 3:
- Chapter 13: Modeling probabilities.
- Chapter 14: Logistic regression pro tips.
- Chapter 15: Building models from the inside out.
Part 4:
- Chapter 16: To understand the past, you must first know the future.
- Chapter 17: Enough about your data. Tell me about the population.
Part 5:
- Chapter 18: How can flipping a coin help you estimate causal effects?
- Chapter 19: Using correlation and assumptions to infer causation.
- Chapter 20: Causal inference is just a kind of prediction.
- Chapter 21: More assumptions, more problems.
Part 6:
- Chapter 22: Who's got next?
· Appendixes: - Appendix A: R quick start. - Appendix B: Thesc are our favorite workflow tips; what are yours? In this book we present many methods and illustrate their use in many applications; we also try to give a sense of where these methods can fail, and we try to convey the excitement the first time that we learned about these ideas and applied them to our own problems.