1: The Language of Statistical Decisions
2: The Language of Probability Models
3: Probability models derived from Bernoulli trials
4: Significance, Power, and $p$-values
5: Independence and Conditional Probability
6: Bayesian Inference for Random Events
In this section, we strive for the standard communicated by this quote, adopted from Natasha Sarin’s interview on the Ezra Klein podcast.
Q: Is the glass half-empty or half full? A: This 8 ounce glass contains 4 ounces of water, plus or minus 0.03 ounces.
8: Continuous Random Variables; Expectation and Standard Deviation, revisited.
9: Z and the Central Limit Theorem; t and small sample means
10: Transformations of data and confidence intervals.
10 (alternative): Bayesian inference for parameter estimation
11: Correlation (which does not necessarily imply causation!) and Regression
12: Recognizing and interpreting outliers