Programme outline
Learning objectives
- Apply hypothesis testing principles, including the interpretation of p-values (probability values) and test statistics.
- Implement parametric testing methods such as the Z-test, T-test (Student’s t-test) and ANOVA(Analysis of Variance) using Python.
- Implement non-parametric tests such as the Wilcoxon Signed Rank and Chi-Square using Python.
- Assess data normality using graphical and numerical approaches.
- Validate the appropriateness of a selected statistical model.
Day 1
- Hypothesis Testing – Intuition of hypothesis testing, P-value & significance level, test statistics & critical value, confidence intervals, type I and II errors
- Parametric testing – Z-test, T-test, Pearson-correlation, ANOVA, ANCOVA
- Normality testing – Graphical and numerical approaches
Day 2
- Data transformation – Log-transformation, Normalisation, Standardisation
- Non-parametric testing – Wilcoxon signed rank, Mann-Whitney U, Chi-Square, Kruskal-Wallis, Friedman, Spearman-Correlations.
- Tests for Independence – Chi-Square, Fisher’s exact test
Day 3
- Project consultation
- Project presentation
Mode of assessment
- Assignment
- Project