Hypothesis testing in R

Open to
Government analysts
Training category
Type of training
Four to six hours
Analysis Function Capability Team
Analysis Function Capability Team


This is a short self-study course on hypothesis testing. It mainly covers the type of errors while:

  • conducting a hypothesis test
  • calculating the probability of those errors
  • conducting a power analysis to find the power and sample size of a test

Firstly, the theory will be shown, then an example of how to apply the corresponding theory in R will be shown, and lastly, there will be an exercise for learners to be completed.

The course is aimed at someone who is comfortable using tidyverse in R and knows statistical tests (such as t-test and chi-square test) and how to run those tests in R, but is interested in:

  • learning about conducting power analysis in R
  • the type of errors in hypothesis testing
  • calculating the probability of errors in hypothesis testing

The prerequisite of this course is: Introduction to R and Statistics in R (if you have not gone through Statistics in R course then look at the “Statistical_tests” file in the “pre_course_information” folder).

Learning outcomes

By the end of the session, participants will:

  • understand what Type I and Type II errors are
  • know the relationship between alpha and Type I error and calculate the probability of Type I error
  • know why effect size is required and calculate the effect size
  • know the relationship between Beta and Type II error
  • calculate the power of the test and calculate the probability of Type II error
  • calculate the sample size given alpha, power and effect size

How to book

Please use your Learning Hub account to access the course online. Alternatively, please email Data.Science.Campus.Faculty@ons.gov.uk.


If you would like more information about this course, please email Data.Science.Campus.Faculty@ons.gov.uk. 

Related courses

Introduction to R

Hypothesis testing in Python