Introduction to Bias, Confounding, and Missing Data
This course equips learners with the tools to identify and manage threats to research validity. Across six lessons, participants explore the core forms of bias and confounding, work through real-world case studies on selecting and controlling for confounders, distinguish effect modification from confounding, apply stratified analysis techniques in statistical software, and learn best practices for diagnosing and handling missing data.
Spot the errors. Adjust the analysis. Trust your results.
This course equips learners with the tools to identify and manage threats to research validity. Across six lessons, participants explore the core forms of bias and confounding, work through real-world case studies on selecting and controlling for confounders, distinguish effect modification from confounding, apply stratified analysis techniques in statistical software, and learn best practices for diagnosing and handling missing data.
Distinguish selection bias, information bias, and confounding, and understand how each distorts the true relationship between exposure and outcome.
Apply directed acyclic graphs, stratified analysis, and propensity score methods to identify confounders and control for them appropriately.
Distinguish genuine subgroup variation (effect modification) from bias, and learn when to report stratified estimates instead of a single crude result.
Identify the causes and types of missingness (MCAR, MAR, MNAR), and apply appropriate handling methods from complete case analysis to multiple imputation, to reduce bias and preserve statistical power.
Know exactly what to do next.
Lessons are organized into short, completable steps so learners can make progress even during a busy clinical or research week.
Small supports that keep learners moving.
Clear steps make the course easier to finish alongside clinical and research duties.
Use course examples and checklists to apply learning in real research workflows.
English and French course support helps teams learn across language contexts.
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