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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