Data Science: Further Hypothesis Testing
Tutor: Dr John Pinney
Course Level: Level 2
Course Credit: 1 credit
Prerequisites: Introduction to Sampling and Hypothesis Testing.
Duration: 2 hour session
Format: Microsoft Teams with live teaching and hands-on practice
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.
- The t-test and its variations
- Comparing variances
- Testing for normality
- Non-parametric testing
- Goodness of fit
- Multiple testing corrections
- Choosing appropriate statistical methods
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
- Thursday 21 October 2021, 09:30-12:30, MS Teams - Python Version
- Friday 19 November 2021, 13:30-16:30, MS Teams - R Version
- Wednesday 09 February 2022, 09:30-12:30, MS Teams - Python Version
- Wednesday 16 March 2022, 09:30-12:30, MS Teams - R Version
- Tuesday 10 May 2022, 09:30-12:30, MS Teams - Python Version
- Tuesday 21 June 2022, 09:30-12:30, MS Teams - R Version
Please select a date and book on via Inkpath using your Imperial Single-Sign-On.