Hypothesis testing in R

Open to
Government analysts
Training category
Type of training
4 to 6 hours
Data Science Campus Faculty
Data Science Campus Faculty

This is a short self-study course on hypothesis testing. It mainly covers the type of errors that can happen 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

The theory will be shown first. Then an example of how to apply the corresponding theory in R will be shown. Lastly, there will be an activity for all participants to complete.

Who this course is for

The course is aimed at people who are interested in:

  • learning about conducting power analysis in R
  • learning about the type of errors that can happen in hypothesis testing
  • knowing how to calculate the probability of errors in hypothesis testing

You should already be comfortable using tidyverse in R. You should also know how to run statistical tests in R, including t-tests and chi-square tests.

Before enrolling on this course you must have completed the following courses:

If you have not completed the ‘Statistics in R’ course, please look at the “Statistical_tests” file in the “pre_course_information” folder.

Learning outcomes

On this course you will learn:

  • what Type I and Type II errors are
  • about the relationship between alpha and Type I error
  • how to calculate the probability of Type I error
  • why effect size is needed and how to calculate the effect size
  • about the relationship between Beta and Type II error
  • how to calculate the power of the test, and calculate the probability of Type II error
  • how to calculate the sample size given alpha, power and effect size

How to book

Please use your Learning Hub account to access the course online. If you do not have a Learning Hub account, 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