10.2. Learning Objectives#

Here is what you should understand after this week

Conceptual#

After this week you should:

  • Understand what a null and alternative hypothesis are
  • Understand the procedure for null hypothesis testing (ie assume the null is true, work out the probability of our test statistic arising)
  • Understand the terms Type I Error and Type II Error
  • Understand that the null distribution is the distribution of the test statistic under the null hypothesis
  • Understand that the null distribution can be estimated empirically by permutation of the sample
  • Understand which datapoints may be permuted to test for:
    • A difference of means in independent samples
    • A mean (pairwise) difference in paired samples
    • A correlation

      The conceptual material is covered in the lecture and recapped in the worked examples in Python

Python skills#

The key skill practiced this week is running a permutation test using scipy.stats

To do that, you will need to be able to create very simple functions

You will also see how to build permutation tests ‘from scratch’ and should understand how to do this but would not bbe expected to create a totally new example yourself

This material is covered in the Jupyter Notebooks in this section