Schedule (topic flow)

A 15-week pacing plan — Blackboard is authoritative for dates and deadlines

Important

This is a pacing plan, not a calendar. It shows the order in which inferential topics unfold and roughly when, so you can read ahead and see where you are headed. It is not the authoritative calendar: exact dates, deadlines, and any mid-term adjustments live in Blackboard (the LMS), which is authoritative. A few fixed semester dates are noted for orientation only — confirm everything against Blackboard.

The course runs fifteen weeks in four parts. Each part below lists its weeks, the theme for each week, and the throughline — the inferential question that carries the week. The theme links to that week’s note.

Part I — The inferential problem (Weeks 1–4)

We set up the frame: what inference is, how sampling variability behaves, and how we judge an estimator.

Week	Theme	The throughline
1	What statistical inference is	How does a sample let us say something responsible about an unknown parameter or claim?
2	Sampling distributions & simulation	What would happen to our estimate if we repeated the study many times?
3	Estimators & standard errors	What is a good estimate, and how do we measure how much it would vary?
4	Bias, variance & MSE	What makes one estimator better than another for a purpose?

Note: Labor Day (Mon, Sep 7) falls in Week 3 — no class that Monday, so the week runs Wednesday and Friday.

Part II — Likelihood & classical inference (Weeks 5–9)

We use likelihood to compare parameter values, then build the classical machinery of intervals and tests.

Week	Theme	The throughline
5	Likelihood	Which parameter values make the data we actually saw most plausible?
6	Maximum likelihood estimation	How do we find the single parameter value the data most support?
7	Confidence intervals (midterm)	What does “95% confident” actually mean, and what does it not mean?
8	Hypothesis tests & p-values	How surprising is our data under a null model — and what does a p-value really say?
9	Error rates, power & decisions	What are we risking when we decide, and how often will we be wrong?

Note: the midterm is Friday, Oct 9 (in class), during Week 7. It covers sampling distributions through confidence intervals. No graded content appears on this public site — see Blackboard.

Part III — Simulation-based inference (Weeks 10–11)

We estimate uncertainty and weigh evidence by resampling and relabeling, with minimal model assumptions.

Week	Theme	The throughline
10	Bootstrap inference	How can resampling our own data estimate how much an estimate would vary?
11	Randomization & permutation tests	How do we test a claim by shuffling labels under a null of no effect?

Part IV — Bayesian inference & synthesis (Weeks 12–15)

We add the Bayesian lens, compare all four frameworks, then build and present a method comparison.

Week	Theme	The throughline
12	Bayesian inference	How does a prior become a posterior, and what is a credible interval?
13	Comparing inferential frameworks	When the four lenses answer the same question, where do they agree and differ?
14	Inference project workshop	How do you compare two inferential methods on one problem and report it responsibly?
15	Final review & synthesis	How do estimation, likelihood, testing, resampling, and Bayes fit as one practice?

Note: fall break is Nov 22–28 (no classes), between Weeks 13 and 14. The last class is Mon, Dec 7, the consultation day is Dec 8, and the final-exam window is Dec 9–15 (exact block via Blackboard).

Note

Again: this page is a pacing plan to help you read ahead. For the real calendar — every date, every deadline, and any adjustment during the term — Blackboard (the LMS) is authoritative.