Hypothesis testing in Python
- Open to
- Government analysts
- Training category
- Analytical, Data science
- Type of training
- Online
- Length
- 4 to 6 hours
- Organiser
- Data Science Campus Faculty
- Provider
- Data Science Campus Faculty
- Location
- Online
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.
Contact
If you would like more information about this course, please email Data.Science.Campus.Faculty@ons.gov.uk.