Hypothesis testing in Python

Open to
Government analysts
Training category
Analytical, Data science
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
  • sample size of a test

Who this course is for

To enrol on this course you need to have completed the Introduction to Python course. It is also useful to have knowledge of statistical tests, such as t-test and chi-square test, before taking part in this course.

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 contact 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. 

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