Overview
This course develops statistical inference for biological, medical, and public-health research. Topics include probability models, sampling distributions, estimation, confidence intervals, hypothesis testing, p-values, statistical power, sample-size planning, one- and two-sample procedures, paired data, proportions, contingency tables, nonparametric methods, and analysis of variance.
Students apply inferential methods to cross-sectional, cohort, case-control, and randomized study designs. Emphasis is placed on interpreting means, mean differences, risk ratios, odds ratios, rate ratios, and confidence intervals; distinguishing association from causation; and evaluating bias, confounding, missingness, measurement error, validity, and generalizability.
Software-based practical work uses biomedical and population-health datasets to develop skills in data preparation, assumption checking, reproducible analysis, graphical communication, and transparent reporting. Ethical principles concerning human participants, privacy, equity, and responsible interpretation are integrated throughout the course.
Learning Outcomes
- Select statistical inference methods appropriate to research questions, study designs, and data structures.
- Conduct estimation and hypothesis tests for means, proportions, associations, rates, paired observations, and multiple-group comparisons.
- Interpret p-values, confidence intervals, effect measures, and statistical power in clinical and population-health contexts.
- Plan sample sizes and evaluate precision, power, and the practical implications of study design decisions.
- Diagnose assumptions, sources of bias, confounding, missingness, and measurement error in biomedical data analyses.
- Evaluate the validity, generalizability, and causal limitations of findings from observational and randomized studies.
- Implement reproducible software-based analyses using appropriate data preparation, diagnostics, and graphical methods.
- Critique research reports and communicate statistical evidence accurately without overstating conclusions.
- Apply ethical principles relating to human subjects, privacy, equity, and transparent statistical reporting.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Fortnightly | All semester |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Data analysis assignments (4 × 7.5%) | 30.00% |
| Assignment | Article-style critique | 10.00% |
| Test | Practical data analysis test | 20.00% |
| Exam | Mid-semester examination | 15.00% |
| Exam | Final examination | 25.00% |
Prerequisites
Teaching Staff & Programs
This course is delivered jointly by faculty from the participating programs listed below. In line with the Douchewater Way, the University of Sexology tailors core instruction directly to each cohort's specific discipline — adapting curriculum to program needs rather than forcing students into a one-size-fits-all model. Learn more about our approach at The Douchewater Way.
