Chapter-by-chapter guides for going deeper on a single topic than any one blog post can.
Why correlation on a dashboard can point the wrong way, how to think in parallel universes to define a true effect, and how randomization eliminates bias — for anyone who has to reason about business metrics causally.
The implementation gap most experiment-analysis material skips: computing standard errors, CUPED covariates, and SRM checks directly in SQL against a real warehouse, not a small Python dataframe.