01
Statistical thinking
Start by understanding data, uncertainty, variation, populations, samples, variables and the purpose of statistical reasoning.
Statistics pathway
Learn statistics from foundations to interpretation.
This pathway helps students move from basic statistical language to descriptive summaries, probability, inference and regression. It is designed for learners who want structure before moving into applied data analysis or research methods.
Suggested route
02
Descriptive statistics
Learn how to summarise data using tables, graphs, measures of centre, spread, percentiles, shape and group comparisons.
03
Probability foundations
Build the language of chance, events, probability rules, conditional probability, independence and diagnostic reasoning.
04
Statistical inference
Move from sample data to uncertainty-aware conclusions using sampling distributions, standard error, confidence intervals and hypothesis testing.
05
Regression foundations
Study relationships between variables, simple regression, residuals, multiple regression, confounding and logistic regression.
Recommended resources
Read these guides alongside the pathway.
Statistics · Foundation
How to choose the correct statistical test
6 min read · Updated 5 June 2026
A detailed guide for students deciding between t-tests, ANOVA, chi-square tests, correlation, regression, logistic regression and non-parametric methods.
Statistics · Foundation
Understanding p-values, confidence intervals and effect sizes
6 min read · Updated 5 June 2026
A detailed guide explaining statistical significance, uncertainty, effect size, practical importance and how students should interpret results responsibly.
Regression · Foundation
Choosing between correlation and regression
6 min read · Updated 5 June 2026
A detailed guide helping students understand when to use correlation, when to use regression, and why the research question matters more than the software menu.
Regression · Intermediate
Linear regression assumptions and diagnostics
5 min read · Updated 5 June 2026
A detailed guide to the assumptions behind linear regression, why they matter, how students should think about diagnostics and how to report limitations clearly.
Research methods · Advanced
Sample size, power and precision explained
6 min read · Updated 5 June 2026
An advanced guide explaining sample size, statistical power, precision, effect size, uncertainty and why planning should focus on estimation as well as hypothesis testing.
Statistics · Advanced
Multiple testing and false discovery rate
5 min read · Updated 5 June 2026
An advanced guide explaining why repeated hypothesis testing increases false positives, how family-wise error and false discovery rate differ, and how to report multiple-testing corrections.
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