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Statistics & Probability for Medical Researchers

Statistics & Probability for Medical Researchers

Practical online course teaching statistics and probability for medical & healthcare researchers. Learn probability theory, hypothesis testing, regression, survival analysis, power/sample-size, and reproducible workflows (R/Python/Excel). Apply methods to clinical data, interpret results correctly, and produce publishable analyses.

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Expiry period Lifetime
Made in English
Last updated at Thu Sep 2025
Level
Beginner
Total lectures 50
Total quizzes 0
Total duration 0 Hours
Total enrolment 3
Number of reviews 0
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Short description Practical online course teaching statistics and probability for medical & healthcare researchers. Learn probability theory, hypothesis testing, regression, survival analysis, power/sample-size, and reproducible workflows (R/Python/Excel). Apply methods to clinical data, interpret results correctly, and produce publishable analyses.
Outcomes
  • Explain fundamental probability concepts and common probability distributions (Bernoulli, Binomial, Poisson, Normal).
  • Calculate and interpret descriptive statistics and graphical summaries for clinical datasets.
  • Formulate research hypotheses and choose appropriate significance tests (t-test, chi-square, nonparametric alternatives).
  • Compute and interpret confidence intervals, p-values, and effect sizes; explain their limitations.
  • Design and perform basic sample-size and power calculations for common study types.
  • Fit, check and interpret linear and logistic regression models including adjustment for confounders and interaction terms.
  • Perform and interpret survival analysis basics (Kaplan–Meier curves, log-rank test, Cox proportional hazards model).
  • Evaluate diagnostic test accuracy using sensitivity, specificity, predictive values and ROC curves / AUC.
  • Apply basic Bayesian reasoning for clinical decision-making (conceptual introduction).
  • Build reproducible analysis workflows (scripted analyses, versioning, literate programming) and prepare transparent statistical reporting for manuscripts or protocols.
  • Recognize common pitfalls (multiple comparisons, selective reporting, collider bias) and how to mitigate them.
  • Translate statistical results into clear clinical conclusions and communicate uncertainty effectively to non-statistical audiences.
Requirements
  • Basic comfort with algebra (fractions, exponents, solving for x) and reading graphs.
  • Familiarity with spreadsheet software (Excel or Google Sheets).
  • Computer with internet access. Recommended: ability to install software (R/RStudio or Python + Jupyter).
  • Recommended but not required: prior exposure to basic descriptive statistics (mean, median, SD).
  • Optional: access to a statistical package — course provides cloud notebooks if you cannot install software.
  • Commitment: 6–10 hours per week (lectures, labs, readings, assignments).