Introduction to Sampling & Hypothesis Testing

This course provides an introduction to the theory of probability and random variables, as well as the statistical theory of sampling, parameter estimation and hypothesis testing. The class is taught on whiteboard to properly introduce the theoretical and mathematical concepts, followed by a series of exercises either with Python or R. However, no prior programming experience is required.

Roadmap of the course:

• Fundamentals of probability theory, random variables and distributions
• Sampling from a distribution, parameter estimation
• The law of large numbers and the central limit theorem
• Expected values, momenta of a distribution (mean, variance) and confidence intervals 
• Introduction to hypothesis testing

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

Learning Outcomes:

On completion of this workshop you will be able to:

• Define probability and random events
• Identify different probability distributions
• Recognise sampling constraints, central tendency and variability
• Employ skills to build and compute estimator quantities and confidence intervals
• Understand and apply correct statistic tests for hypothesis testing
• Assess numerical results to make statistical inferences

Prerequisites

Knowledge of basic statistical concepts.

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.