Syllabus (public companion)
Orientation and course shape — Blackboard is authoritative for graded specifics
This public page is an orientation companion to the syllabus, not the operational syllabus itself. It describes the shape of the course in qualitative terms. It carries no numeric grade weights, no schedules, and no submission rules. For everything that counts toward your grade — exact weights, deadlines, policies, and submissions — Blackboard (the LMS) is authoritative. If this page and Blackboard ever disagree, follow Blackboard.
Course description
This course studies statistical methods that stay useful when standard parametric assumptions are questionable, hard to justify, or not the main point of the analysis. Real data are often skewed, heavy-tailed, ordinal, small-sample, or contaminated by outliers. We begin with the question what can we responsibly infer with weaker assumptions? and then study empirical distributions, order statistics, ranks, permutation logic, randomization tests, bootstrap distributions and confidence intervals, rank-based one-sample/paired/two-sample methods, categorical and ordinal outcomes, robust summaries, outliers, robust regression ideas, and simulation studies that compare method behavior. The course does not treat nonparametric methods as a box of backup tests to use only after a normality test fails; it treats resampling, ranks, robustness, and simulation as core statistical ideas. The emphasis throughout is reasoning, computation, comparison, and interpretation — learning how methods behave, what assumptions remain, how to choose a method for a purpose, and how to communicate results without overselling them.
Who it is for
This course assumes a prior introductory statistics course or comparable preparation: you should be comfortable with variables, data tables, graphical and numerical summaries, confidence intervals, hypothesis tests, p-values, and basic regression interpretation. Prior probability or inference and prior R are helpful but not required — the course reviews the repeated-sampling ideas it needs and scaffolds the code. The main expectation is a willingness to reason about assumptions, write short explanations, compare methods, and revise an interpretation after checking a simulation or output.
Learning outcomes
By the end of the course, a successful student will be able to:
- Explain why assumption-light methods are useful in applied statistical work.
- Distinguish parametric, nonparametric, resampling-based, randomization-based, and robust approaches.
- Use empirical distributions, ranks, order statistics, and quantiles to summarize data.
- Explain the logic of permutation tests and randomization tests, and conduct and interpret randomization tests for simple comparison settings.
- Use bootstrap resampling to approximate sampling variability, construct and interpret bootstrap confidence intervals, and explain what the bootstrap assumes and when it may fail.
- Apply and interpret rank-based methods for one-sample, paired, and two-sample problems, and analyze ordinal or categorical outcomes with methods appropriate to the measurement scale.
- Compare means, medians, trimmed means, and other robust summaries; identify outliers and high-influence observations without treating every unusual value as an error.
- Explain the idea of robust regression and why least-squares regression can be sensitive to unusual observations.
- Use simulation studies to compare method behavior, compare parametric and nonparametric conclusions without treating one as automatically “correct,” and communicate assumption-light analyses clearly.
Weekly rhythm
This is a lecture/activity course meeting three days a week, and each day has a settled role:
- Monday — method concept + checkpoint. We introduce a resampling, nonparametric, or robust-methods idea, work through examples, and complete a short checkpoint near the end of class.
- Wednesday — computation or simulation day. We implement the method, run simulations, inspect output, visualize behavior, or compare methods under different assumptions.
- Friday — quiz / comparison day. A short quiz on recent material, then we compare parametric and nonparametric conclusions, critique an analysis, or work on a short report component.
This rhythm is the plan; the authoritative weekly schedule, including any shifts, lives in Blackboard. Attendance is not graded directly, but checkpoints, quizzes, labs, and in-class activities happen during class, so consistent attendance matters — the course builds intuition by repeated comparison of formula, simulation, output, and interpretation.
Assessment shape (indicative — not a contract)
The table below conveys the relative emphasis of each graded category in qualitative terms only. It is not a grading contract: it carries no percentages and no point values, and the actual weights live in Blackboard.
| Category | Rough emphasis |
|---|---|
| Method checkpoints | small |
| Weekly quizzes | small |
| Homework and method reports | the largest single category |
| Resampling and robustness labs | moderate |
| Midterm | moderate |
| Applied robust-methods project | moderate |
| Final exam | small |
Read this as a picture of where the weight sits: regular homework and method reports are the backbone of the grade, the labs and the two big instruments (midterm, project) carry moderate weight, and the smaller pieces — checkpoints, quizzes, and the final — add up around them. For the exact weighting, consult Blackboard.
Software and reproducibility
We use R (through RStudio or Posit Cloud) and Quarto, along with spreadsheets and browser-based tools, for permutation and randomization tests, bootstrap intervals, rank methods, robust summaries, outlier sensitivity, and simulation studies. Computation supports the reasoning rather than replacing it: you read, edit, and run code; interpret output; and write short written conclusions. When software is used, you are expected to explain what the code is doing, what is being resampled or ranked, what assumptions remain, and how the output supports the conclusion. Setup instructions are on the resource pages.
AI use (summary)
Generative AI tools may be used as a study and workflow aid — to explain a concept a second way, generate practice questions, help debug simulation code, suggest ways to visualize a result, or check your understanding of a resampling method. They may not produce work you submit as your own, complete homework solutions, write project explanations, fabricate simulation results, invent interpretations, or replace your responsibility to understand and verify the reasoning, and they are prohibited on quizzes and exams unless explicitly allowed.
Because resampling and nonparametric methods are highly sensitive to the data-generating process and the resampling scheme, AI output must be checked carefully. Many incorrect explanations come from permuting the wrong thing, resampling rows when the dependence structure should be preserved, treating a bootstrap interval as assumption-free, confusing ranks with raw values, ignoring outliers without justification, or claiming a nonparametric method has no assumptions. Whenever you use AI on a graded written assignment, lab, code submission, or project component, include a brief AI Use Note with three labeled lines:
| Field | What to record |
|---|---|
| Tool | which assistant you used (with approximate date or version) |
| Purpose | what you used it for |
| Verification | how you checked, tested, revised, or validated the output |
Verification is the load-bearing line: rerun the code, check the resampling scheme, compare a small hand-worked example to the output, confirm what is held fixed under the null, check assumptions against the notes, test results under a simulation, or rewrite the explanation in your own words after checking it. The full policy lives in Blackboard.
Materials
You will need:
- Instructor notes, examples, and method guides — the primary course materials, posted on this site and in Blackboard.
- Introduction to Modern Statistics, 2nd ed. (Çetinkaya-Rundel & Hardin) — a free supplement, CC BY-SA 3.0, at openintro-ims.netlify.app, used for the permutation/bootstrap concepts.
- Statistical Inference via Data Science: A ModernDive into R and the Tidyverse, 2nd ed. (Ismay, Kim & Valdivia) — a free supplement, CC BY-NC-SA 4.0, at moderndive.com/v2, used for the resampling R workflow.
- Nonparametric Statistical Methods, 3rd ed. (Hollander, Wolfe & Chicken, Wiley) — an optional classical reference; named and cited only.
- R, RStudio or Posit Cloud, and Quarto (plus spreadsheets or browser tools) — for the labs and simulations.
- Blackboard (the LMS) — for all graded work, dates, and announcements.
- A non-graphing scientific calculator for in-class quizzes; most computation is done in R.
Not used in this course: Cengage, WebAssign, MyLab, or any paid homework platform.
Where things live
Keep the two homes of the course straight:
- Blackboard (the LMS) is the operational home: graded method checkpoints, quizzes, homework and method reports, resampling and robustness labs, the midterm, the applied robust-methods project, the final exam, all due dates, all submissions, and all grades. It is authoritative.
- This public site is the public notes home: weekly notes, labs as study material, resources, and orientation pages. It is ungraded.
This companion exists to orient you. Whenever a graded specific is at stake — a weight, a deadline, a policy — go to Blackboard, which is authoritative.