Basic Statistics
Introduction to statistics for PhD students and beyond
About this course
Basic Statistics for Researchers is a self-paced, R-based guide that bridges the gap between introductory statistics and being able to design, run and interpret analyses on real clinical and biological data.
Learning objectives
- Set up an R-based statistical workflow
- Run and interpret hypothesis tests, estimation and confidence intervals
- Fit and interpret linear and logistic regression models
- Apply mixed models and survival analysis where appropriate
- Choose the correct method for a given study design
- Understand the basics of causal inference
Who it is for
PhD students, clinicians, epidemiologists and biomedical researchers who want practical statistical analysis skills without heavy mathematical prerequisites.
Curriculum
- Setup and workflow fundamentals
- Inference, testing and estimation
- Hypothesis testing, t-tests, ANOVA, correlation, power analysis
- Regression and modelling
- Linear and logistic regression
- Advanced methods
- Mixed models, survival analysis, causal inference
- Reference materials and decision guides
Format & materials
The primary resource is an online book, maintained as an open, self-paced guide. The repository accepts corrections and suggestions via issues and pull requests.
Course site: https://sufyansuleman.github.io/basic_statistics/
Citation: Suleman, S. (2026). Basic Statistics for Researchers. Zenodo. https://doi.org/10.5281/zenodo.20672940
Run this course for your group
I teach Basic Statistics on demand for research groups, departments and companies, on site or online, adapted to your data and level.