Further Hypothesis Testing

Building on the material covered by Introduction to Sampling and Hypothesis Testing, this course explores in detail the field of of hypothesis testing, and how to apply it to data sets that may deviate from theoretical distributions. It also covers introduction to conditional and Bayesian probability.

Roadmap of the course:

• Recap on parameter estimation and prediction vs inference
• Hypothesis testing: The t-test and F-test to compare two sets of observations ANOVA andchi squared to test for normality and evaluate goodness of a fit
• Parametric vs non-parametric testing
• Multiple testing corrections, interpretation of p-value
• Choosing appropriate statistical methods

This course is open to Research Degree Students, Postdocs & Research Fellows. Limited spaces available for wider Imperial community.

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.

Prerequisites

Introduction to Sampling & Hypothesis Testing

How to book

• Early Career Researchers (Research Degree Students, Postdocs, Research Fellows) should book via Inkpath using your Imperial Single-Sign-On.
• All other members of the Imperial community, should book here.

Please ensure you have read and understood ECRI’s cancellation policy before booking.