A self-paced online course, Basics of Statistical Analysis for Research, for learners in healthcare, life sciences and biomedicine is available.
The course is intended for undergraduate and graduate students, doctoral researchers, and early-career researchers who want to strengthen their understanding of statistical analysis. No advanced mathematical knowledge or previous experience with statistical software is required.
Rather than introducing statistics through formulas or a long list of tests, the course starts with realistic research questions and examples from health and biomedical research. Learners are gradually guided from understanding research data and describing what they observe to considering uncertainty, comparing groups, examining relationships between variables, and interpreting and reporting results.
Examples throughout the course include biomarker studies, screening, patient outcomes, laboratory measurements and other common research situations. The emphasis is on understanding why a particular analytical approach may be appropriate and what the result actually means.
Each module contains short self-paced lessons and automated knowledge checks with explanatory feedback, allowing learners to progress at their own pace.
A central message runs throughout the course: good statistical analysis is not about finding p < 0.05; it is about producing a defensible answer to a research question.