Academy course
Statistics & reproducible analysis
Pick the right test, check assumptions, report effect sizes, and make the whole analysis reproducible.
What this is
Practical applied statistics for researchers who are not statisticians, taught against your own dataset with reproducibility built in from day one.
What you will be able to do
- • Choose tests that match your design and data
- • Check assumptions and act when they fail
- • Report effect sizes and intervals, not p-values alone
- • Publish data, code and a pre-registered analysis plan
Workspace services used
Every module is practised inside services you already have in your account.
Start in workspace — Statistics LabSyllabus
1. Design → test
Comparisons, associations, repeated measures, and what your design allows.
2. Assumptions
Normality, variance, independence, and robust alternatives.
3. Effect sizes
Cohen's d, eta-squared, odds ratios, confidence intervals.
4. Regression
Linear and logistic models, confounding, model checking.
5. Reproducibility
Data dictionaries, pre-registration, version control, sharing an analysis package.
Join the next intake
Cohorts open on a rolling basis and places are limited so feedback stays personal. Create an account to reserve a place, or write to us with your topic and timeline.