Tutors: Dr John Pinney
Course Level: Level 1
Prerequisites: Knowledge of basic statistical concepts. 
Duration: 2 hour session
Format: Microsoft Teams with live teaching and hands-on practice

This course provides an introduction to the statistical theory of sampling, parameter estimation and hypothesis testing.  There is some overlap with the class on Basic Statistics but the concepts are explored in greater detail.  The class is taught either with Python or R examples (see the advertisement for details).


  • prerequisites review - mean, median, mode, variance, standard deviation
  • random variables and distributions
  • normal distributions - properties and standardisation
  • skewed distributions
  • sampling 
  • central limit theorem
  • sampling distribution 
  • sampling variability and standard error
  • standard deviation versus standard error
  • statistical inference
  • confidence intervals
  • hypothesis testing
  • test statistics
  • type I and II errors
  • steps in hypothesis testing process
  • examples

Learning Outcomes:

On completion of this workshop you will be able to:

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

Pre-Course setup


  • Wednesday 12 May 2021, 14:00-16:00, Microsoft Teams (Python version)
  • Tuesday 08 June 2021, 13:00-15:00, Microsoft Teams (R version)