Data 145: Evidence and Uncertainty - Topic Map
My notes on Data 145: Evidence and Uncertainty. This page is a compact topic map for future review and for anyone else looking for a high-level reference.
Course thread: how to turn noisy data into evidence, quantify uncertainty, and make decisions without pretending randomness disappeared.
Phase 1 Notes
Phase 2 Notes
Phase 2 notes Lectures 14-15: Testing, Confidence, and Power Testing structure, confidence intervals, and power. Phase 2 notes Lectures 17-18: Likelihood and Decision Rules Likelihood reasoning and general testing procedures. Phase 2 notes Lectures 19-20: Multiple Testing Bonferroni, FWER, FDP, and FDR. Phase 2 notes Lectures 21-22: MGFs and Concentration MGFs, Chernoff thinking, Hoeffding, and tail bounds. Phase 2 notes Lectures 23-24: Geometry of Linear Models Rotations, nuisance/signal/residual blocks, t, chi-squared, F, ANOVA, and regression. Phase 2 notes Lectures 25-26: Gibbs Sampling and Hierarchical Bayes MCMC, Gibbs full conditionals, burn-in, shrinkage, empirical Bayes, and hyperparameter concentration. Phase 2 notes Lecture 27: Introduction to Causal Inference Potential outcomes, randomized trials, ATE estimation, confounding, propensity scores, and inverse propensity weighting. Final review Compact Cheat Sheet and Test Decision Map A course-wide final review with a complete test-selection map, compact formulas, assumptions, and common exam traps.
Study path: use Phase 1 for the pre-midterm course narrative, Phase 2 as post-midterm topic-by-topic references, and the compact cheat sheet for final-pass review.
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