Key Information
Tutor: Dr Jesús Urtasun Elizari
Course Level: Level 2
Course Credit: 1 credit
Prerequisites: Introduction to Sampling and Hypothesis Testing.
Duration: 3 x 2 hour session
Course Resources
Building on the material covered by Introduction to Sampling and Hypothesis Testing, this workshop will explore the application of hypothesis testing to data sets that may deviate from theoretical distributions.
Syllabus:
- Introduction to parameter estimation, prediction vs inference
- Hypothesis testing: The t-test, F-test, chi squared test
- ANOVA, testing for normality
- Parametric vs non-parametric testing
- Chi square revisited, goodness of fit
- Multiple testing corrections, interpretation of p-value
- Choosing appropriate statistical methods
Learning Outcomes:
After completing this workshop, you will be better able to:
- Compare two samples to demonstrate significant differences in their distributions.
- Explain the difference between parametric and non-parametric testing.
- Assess the goodness of fit between a model distribution and the observed data.
- Apply a multiple-testing correction to a p-value calculation.
- Select a test that is suitable for a given statistical question.
Dates & Booking Information
- Monday 16 December 2024 (Part 1), Wednesday 18 December 2024 (Part 2) & Thursday 19 December 2024 (Part 3), 13:30-15:30, South Kensington (In-Person Teaching)
- Monday 31 March 2025 (Part 1), Wednesday 02 April 2025 (Part 2) & Thursday 03 April 2025 (Part 3), 10:00-12:00, South Kensington (In-Person Teaching)
- Monday 16 June 2025 (Part 1), Thursday 19 June 2025 (Part 2) & Friday 20 June 2025 (Part 3), 14:00-16:00, South Kensington (In-Person Teaching)
To book your place, please follow the booking process advertised on the main programme page