01
Health data questions
Start by identifying the clinical, public-health or biomedical question and the type of outcome being studied.
Biostatistics pathway
Learn biostatistics through health data and evidence.
This pathway helps students connect statistical methods with clinical and biomedical questions. It focuses on study design, health outcomes, uncertainty, regression, diagnostic reasoning and responsible interpretation.
Suggested route
02
Study design
Understand trials, cohort studies, case-control studies, cross-sectional studies, bias, confounding and eligibility criteria.
03
Medical statistics
Build interpretation skills for risk, rates, confidence intervals, p-values, effect sizes and uncertainty in health evidence.
04
Regression and adjustment
Learn how regression models are used to adjust for covariates, describe associations and support clinical interpretation.
05
Clinical interpretation
Move beyond significance and focus on practical relevance, uncertainty, limitations and responsible reporting.
Recommended resources
Read these guides alongside the pathway.
Biostatistics · Intermediate
Logistic regression explained for health and social science students
6 min read · Updated 5 June 2026
A detailed guide to logistic regression for binary outcomes, including odds, odds ratios, interpretation, adjustment, limitations and common reporting mistakes.
Research methods · Advanced
Sample size, power and precision explained
6 min read · Updated 5 June 2026
An advanced guide explaining sample size, statistical power, precision, effect size, uncertainty and why planning should focus on estimation as well as hypothesis testing.
Biostatistics · Advanced
Survival analysis: Kaplan-Meier curves and Cox regression
5 min read · Updated 5 June 2026
An advanced guide to time-to-event data, censoring, Kaplan-Meier curves, log-rank tests, Cox regression, hazard ratios and careful interpretation in medical research.
Biostatistics · Advanced
Confounding, mediation and effect modification
5 min read · Updated 5 June 2026
An advanced guide explaining three important ideas in observational research: confounding, mediation and effect modification, with examples, interpretation and common mistakes.
Biostatistics · Advanced
Introduction to causal inference and DAGs
6 min read · Updated 5 June 2026
An advanced guide introducing causal questions, counterfactual thinking, directed acyclic graphs, confounding, colliders, mediators and why causal inference is more than regression adjustment.
Biostatistics · Advanced
ROC curves, sensitivity, specificity and AUC
5 min read · Updated 5 June 2026
An advanced guide to diagnostic test evaluation and prediction model performance, covering sensitivity, specificity, thresholds, ROC curves, AUC and limitations.
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If you are unsure which method, design or interpretation route fits your question, submit an enquiry with your subject, level and topic.
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