School of Health Sciences & Professional Programs · Dept. of Health & Human Performance
One question, fifteen weeks, one dataset: why is frequent mental distress higher in Jamaica than in most of New York City — and how would we know that's really true?
An introduction to the statistical methods used to analyze tests, measurements, and data in public health. The emphasis is on interpretation — reading graphical and statistical output, recognizing when a claim about health data is wrong, and explaining why.
Rather than moving through a new topic and a new dataset every week, this course asks one question all semester and returns to it as your tools improve. In Week 1 you will write down what you think the answer is. In Week 14 you will get that paper back.
The question
Why is frequent mental distress higher in Jamaica than in most of New York City — and how would we know that's really true?
Most sessions follow the same shape, so you will usually know what to expect. Exam days and a few others will differ.
We open with a short reading handed out in the room, along with a question to answer as you go. We talk that through together, then work through the idea behind it. When there is data to look at, it goes up on the screen. The largest part of the session is workshop — you, working with a few other people, on something real. We close by pulling it back together as a room.
Nothing is assigned before class. You will not be asked to read anything at home in order to keep up. The reading happens in the room, with everyone, at the start of the session.
We use Python to work with real data, through Google Colab — a free website. There is nothing to install, nothing to buy, and it runs in a browser on any machine, including a library desktop or a phone.
You will not write code in this course. Every notebook is prepared and run before class. You will read what a line of code did and look at what it produced. The work you are graded on is your interpretation of the output.
Grading policy on code
No points are ever deducted for code that does not run. If something breaks, submit your interpretation of the output you were able to see.
CDC PLACES — health estimates for 168 New York City ZIP codes, including frequent mental distress, depression, insurance coverage, general health, and physical activity. One file, all semester. Frequent mental distress means fourteen or more days in the past month when a person reported their mental health was not good; it is a population surveillance measure, not a clinical diagnosis. These are modeled small-area estimates rather than direct counts, which is itself something we will examine in Week 12.
Ward, C. & Nolte, C. (2021). An Intuitive, Interactive Introduction to Biostatistics. University of Iowa. Available at no cost through the Open Textbook Library.
Section references are listed for each week for students who want to go further. You can do very well in this course without opening it. Everything you are assessed on is covered in class.
Fourteen sessions.
New data work a new way of working with the dataset is introduced · Graded audit you analyze a flawed professional memo, and it counts toward your grade
| Date | Concept | Text | |
|---|---|---|---|
| 1 | Sep 2 | What biostatistics is for — and your first answer to the question | 1.1–1.3 |
| 2 | Sep 9 | Where health data comes from, and who it never reaches | 3.1–3.2 |
| 3 | Sep 16 | What a dataset is; levels of measurement New data work | 2.1–2.3 |
| 4 | Sep 23 | Study design: what a study is allowed to claim Graded audit | 2.2 |
| 5 | Sep 30 | Out of whom? Why a count is not a rate New data work | 2.3 |
| 6 | Oct 7 | Populations aren't comparable as they are: age adjustment New data work Graded audit | 3.4 |
| 7 | Oct 14 | Exam 1 — Sessions 1–6, concepts only | — |
| 8 | Oct 21 | A sample is not the population; numbers wobble | 4.1, 6.1–6.3 |
| 9 | Oct 28 | Is the difference real? What a p-value says and doesn't Graded audit | 7.1–7.4 |
| 10 | Nov 4 | Measuring association: relative risk and odds ratio New data work Graded audit | 5.1–5.3 |
| 11 | Nov 11 | Association is not causation; confounding | 3.2 |
| 12 | Nov 18 | People report imperfectly — self-report, undiagnosed cases, estimates | 3.4 |
| — | Nov 25 | No classes scheduled — college closed Nov 26–28 | — |
| 13 | Dec 2 | Tests are wrong sometimes: screening and the prevalence trap Graded audit | 4.1 |
| 14 | Dec 9 | Answering the question. Your September papers come back. | — |
| — | Dec 15–21 | Exam 2 — Sessions 8–13 · Final memo due | — |
Hybrid sessions
This is a hybrid course: some sessions meet in Science 203 and some meet online. The format for each session is posted at least one week in advance — check the course site before every class.
| Component | Weight |
|---|---|
| Participation Entry answers and workshop sheets, collected every session. Graded for completion, not correctness. You write your own sheet, working through it with your group. | 25% |
| GitHub portfolio Built by curating your best in-class work across the semester, with a short framing. Not new work — assembled work. | 20% |
| Exam 1 — Oct 14 Sessions 1–6. Short scenarios and flawed claims: what is wrong, and why. No code, no formulas to memorize. | 15% |
| Exam 2 — finals week Sessions 8–13, same format. | 15% |
| Final memo Two to three pages answering the course question, written for a community board rather than for me. | 15% |
| LinkedIn posts Ten posts connecting course concepts to current public health issues, drafted in class. Graded on consistency and relevance. | 10% |
| Total | 100% |
On attendance
A quarter of your grade is work collected in the room, and none of it can be made up at home. Missing a session costs you that session's points. If something happens, tell me before the class you will miss, not after.
| Sep 3 | Last day to add a course. Last day to drop without a grade of WD. |
| Sep 17 | Census date. Last day to drop without a grade of W. Last day to declare a major effective Fall 2026. |
| Oct 12 | College closed. |
| Oct 13 | Classes follow a Monday schedule — this course does not meet. |
| Nov 6 | Last day to withdraw with a grade of W without CAPS approval. |
| Nov 25 | No classes scheduled. |
| Dec 14 | Last day of classes. |
| Dec 15–21 | Final examinations. |
Refer to the York College Academic Calendar for the complete list. Dates are subject to change.
| Grade | Index | Range | Grade | Index | Range |
|---|---|---|---|---|---|
| A+ | 4.0 | 97–100% | C+ | 2.3 | 77–79.9% |
| A | 4.0 | 93–96.9% | C | 2.0 | 73–76.9% |
| A− | 3.7 | 90–92.9% | C− | 1.7 | 70–72.9% |
| B+ | 3.3 | 87–89.9% | D+ | 1.3 | 67–69.9% |
| B | 3.0 | 83–86.9% | D | 1.0 | 60–66.9% |
| B− | 2.7 | 80–82.9% | F, FIN, WU, Z | 0 | 0–59% |
The mission of York College's undergraduate public health program is to engage in teaching, learning, scholarship, and service to foster and sustain a healthier New York City, and to promote culturally responsive, evidence-informed solutions to reduce disparities and promote health and wellness among urban populations.
If an emergency arises, notify me immediately so we can agree on a course of action, particularly if you are unable to complete the semester. See the York College Academic Calendar for deadlines to drop or withdraw.
Academic dishonesty — including plagiarism, internet plagiarism, obtaining an unfair advantage, and falsification of records and official documents — is prohibited within The City University of New York and is punishable by penalties including failing grades, suspension, and expulsion. Review the University's full policy in the York College Bulletin or at york.cuny.edu.
On AI in this course: AI tools are permitted and encouraged for understanding concepts, explaining output, and checking your reasoning. Written interpretations must be your own analysis. You are responsible for everything you submit, including errors an AI introduced — which is exactly the skill this course is built around.
An INC grade can only be given to a student who, because of extenuating circumstances, has not taken the final examination or completed the coursework and has a passing average. The student has up to 10 weeks in the subsequent semester to complete the work and resolve the grade, even if not registered that semester. Grade changes resolving INC grades must reach the Office of the Registrar by the last day of the tenth week of classes of the subsequent semester. An INC not resolved within that window becomes FIN, which is calculated as an F in the academic index and can only be changed by appeal through CAPS. Students up for graduation cannot graduate until an INC is resolved.
CUNY York College is committed to providing access to programs and services for qualified students with disabilities. If you are a student with a disability and require accommodations to participate in and complete requirements for this class, contact the Center for Students with Disabilities (Academic Core Building, Room 1G02, 718-262-2191) for verification of eligibility and determination of specific accommodations.