01
Probability Foundations
Sample spaces, conditional probability, Bayes' theorem and the building blocks of statistical inference.
Statistics 2Inferring from data.
Statistics 2 is the second course in the statistics sequence and focuses on the principles of statistical inference. Building on the descriptive methods from Statistics 1, the course introduces probability, sampling distributions, hypothesis testing and regression — the tools used to draw conclusions and make predictions from data.
6
core modules
12 wks
contact + labs
Project
real-world analysis
What you'll learn
By the end of this course, students are expected to understand how data is summarised, how uncertainty is quantified, and how statistical evidence is built and reported.
02
Random Variables & Distributions
Discrete and continuous distributions — Binomial, Poisson, Normal — with expectation and variance.
03
Sampling & Estimation
Sampling distributions, the Central Limit Theorem, and point estimators with confidence intervals.
04
Hypothesis Testing
Z-tests, t-tests, chi-square tests, p-values, Type I and Type II errors, and significance.
05
Correlation & Regression
Linear regression, residuals, R², interpreting slope and intercept in real-world data.
06
ANOVA & Non-parametric Tests
Comparing multiple groups with ANOVA and applying rank-based methods when assumptions break.
Learning outcomes
Students completing this course will be able to frame a real-world question as a statistical problem, choose an appropriate test, run the analysis in software and communicate the result honestly.
- Translate a research question into testable hypotheses.
- Apply the Central Limit Theorem to build confidence intervals.
- Run and interpret t-tests, chi-square tests and linear regression.
- Diagnose model assumptions and apply remedies when they fail.
- Communicate findings with clear, honest visualisations.
Tools you'll use
R / RStudioPython · pandasSPSSExcelJupyter
Assessment
Weekly problem sets, two mid-term tests, and a final data-analysis project where each student chooses a dataset and reports the findings in a short paper.
See Statistics 2 in action
On the Data Insights page, I've collected a real dataset of 100 records, embedded the live spreadsheet and shared the observations I drew from it.
Open Data Insights