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:

• The t-test and its variations
• Comparing variances
• ANOVA
• Testing for normality
• Non-parametric testing
• Goodness of fit
• Multiple testing corrections
• 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

• Tuesday 28 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.