Student workbooks and weekly practice
14 weeks of original problems, rubrics and self-checks
Learn to ask good questions, describe distributions, quantify uncertainty, choose appropriate methods and report limitations. Programming is a companion, not a prerequisite.
Course syllabus & weekly plan
Learning outcomes, teaching sequence, psychology/statistics pathways.
R practice & data centre
Synthetic dataset, data dictionary, downloadable base R scripts.
Practice assessment & rubric
Revision tasks and capstone criteria; not official UCL assessment.
First lesson: what is statistics?
A 40–45 minute beginner introduction and five-question quiz.
Mathematical Statistics Foundations
8 worked mathematical units, 24 problems and base R verification
All 15 learning modules
What is statistics?
Bounds of evidence
Research design
Questions and study designs
Data and measurement
Variables, scales and missing data
Descriptive statistics
Centre, spread and visualisation
Probability
Models of randomness
Sampling, intervals and power
Estimation uncertainty and confidence intervals
Hypothesis testing
p-values and effect magnitude
t-tests
Independent and paired comparisons
Correlation
Association versus causation
Regression
Models and residual diagnostics
ANOVA
More than two groups
Chi-square
Categorical association and denominators
Non-parametric tests
Alternative tests and assumptions
Reproducibility and ethics
Transparent code, ethics and limitations
Capstone research project
Independent end-to-end analysis
How this relates to UCL
This independently authored pathway covers topics relevant to introductory psychology statistics and practical R. Psychology's first-year PALS0046 is a possible target; statistics degrees include more mathematical modules such as STAT0002, STAT0003 and STAT0004. This resource is not a module-by-module equivalence claim, and a student's actual timetable has not been verified.