Introduction to Sampling & Hypothesis Testing

This course provides an introduction to 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 (see the dates below for details). However, no prior programming experience is required.

Syllabus:

• Fundamentals of probability theory, random variables and distributions
• Sampling from a distribution, the central limit theorem
• Momenta of a distribution (mean, variance, skewness, kurtosis) 
• Confidence intervals
• Introduction to hypothesis testing
  
  

Learning Outcomes:

On completion of this workshop you will be able to:

• Identify different statistical distributions
• Recognise sampling constraints and variability
• Employ skills to build confidence intervals
• Apply correct test statistics for hypothesis testing
• Assess numerical results to make statistical inferences

Dates & Booking Information

To book your place, please follow the booking process advertised on the main programme page