Data 145: Evidence and Uncertainty

UC Berkeley, Spring 2026

Welcome to Data 145! When we learn about the world from data, how much can we rely on the conclusions we draw? How do we know if we could do better?

Week 12 Announcement

Apr 6 · 1 min read

Here’s what you need to know for week 12:

• Worksheet 9: Due Monday, April 13 at 5 PM on Gradescope
• You can find our staff office hours on the Weekly Calendar and faculty office hours on the Resources page. Please check Ed periodically for updates.

Past announcements

This schedule is tentative. Topics and due dates may be adjusted as the semester progresses. Check Ed Discussion for announcements.

Week 1

Tue, Jan 20	Lecture 1 Course intro; convergence and delta method via MLE Worksheet 1due 1/26
Thu, Jan 22	Lecture 2 Modes of convergence, continuous mapping, Slutsky
Fri, Jan 23	Discussion 1

Week 2

Tue, Jan 27	Lecture 3 MLE 1: Score, Fisher information, consistency Worksheet 2due 2/2
Thu, Jan 29	Lecture 4 MLE 2: Asymptotic normality, confidence intervals
Fri, Jan 30	Discussion 2

Week 3

Tue, Feb 03	Lecture 5 MLE 3: Efficiency, Cramér-Rao Worksheet 3due 2/9
Thu, Feb 05	Lecture 6 Bayes 1: Loss, risk, MSE, bias-variance decomposition
Fri, Feb 06	Discussion 3

Week 4

Tue, Feb 10	Lecture 7 Bayes 2: Prior and posterior, exponential families Worksheet 4due 2/17
Thu, Feb 12	Lecture 8 Bayes 3: Epistemic uncertainty, Jeffreys priors
Fri, Feb 13	Discussion 4

Week 5

Tue, Feb 17	Lecture 9 Model misspecification, K-L divergence Worksheet 5due 2/23
Thu, Feb 19	Lecture 10 Empirical distribution, bootstrap confidence intervals
Fri, Feb 20	Discussion 5

Week 6

Tue, Feb 24	Lecture 11 Empirical CDF, KS statistic, hypothesis testing intro Worksheet 6due 3/2
Thu, Feb 26	Lecture 12 Hypothesis testing 1: Likelihood ratios, Neyman-Pearson
Fri, Feb 27	Discussion 6

Week 7

Tue, Mar 03	Lecture 13 Hypothesis testing 2: LRT examples, UMP tests Worksheet 7due 3/9
Thu, Mar 05	Lecture 14 Hypothesis testing 3: p-values, test-CI duality
Fri, Mar 06	Discussion 7

Week 8

Tue, Mar 10	Lecture 15 Asymptotics: Score, Wald, generalized LRT
Thu, Mar 12	Lecture 16Midterm review
Fri, Mar 13	Exam Midterm Exam (7–9 PM, Latimer 120)

Week 9

Tue, Mar 17	Lecture 17 Generalized LRT, chi-squared test for goodness of fit Worksheet 8due 3/30
Thu, Mar 19	Lecture 18 A/B testing: Comparing samples, permutations
Fri, Mar 20	No Section

Week 10

Tue, Mar 24	Spring Break
Thu, Mar 26	Spring Break
Fri, Mar 27	Spring Break

Week 11

Tue, Mar 31	Lecture 19 Multiple testing 1: FWER, Bonferroni
Thu, Apr 02	Lecture 20 Multiple testing 2: FDR, Benjamini-Hochberg
Fri, Apr 03	Discussion 8

Week 12

Tue, Apr 07	Lecture 21 Finite sample concentration, sub-Gaussian variables Worksheet 9due 4/13
Thu, Apr 09	Lecture 22 Applications
Fri, Apr 10	Discussion 9

Week 13

Tue, Apr 14	Lecture 23 Multivariate Gaussian 1: Z, chi-squared, t, F distributions Worksheet 10due 4/20
Thu, Apr 16	Lecture 24 Multivariate Gaussian 2: Multiple regression inference
Fri, Apr 17	Discussion 10

Week 14

Tue, Apr 21	Lecture 25Empirical Bayes, local FDR, Gibbs sampler Worksheet 11due 4/27
Thu, Apr 23	Lecture 26Causal inference 1
Fri, Apr 24	Discussion 11

Week 15

Tue, Apr 28	Lecture 27Causal inference 2
Thu, Apr 30	Lecture 28Course summary
Fri, May 01	No Section

Week 16

Tue, May 05	RRR Week
Thu, May 07	RRR Week
Fri, May 08	RRR Week

Week 17

Tue, May 12
Wed, May 13	Exam Final Exam, 11:30 am - 2:30 pm
Thu, May 14
Fri, May 15