21/03/2023
For AI HL and SL, a bare minimum outline of the key things you need to know about the main hypothesis tests. Generally, the idea is that the tests give you a value (chi-squared or t-value) where the higher it is, the less likely your null hypothesis is true. Unfortunately, most of the time we use the p-values and the significance levels, so LEARN the inequalities for how to draw conclusions!
You also need to know where to put the data/statistics in and where to find the tests on your GDC, and watch out for how to phrase the hypotheses and your conclusions - this is a good one to read the Mark Schemes carefully to see what phrases the examiners need to see to award the final marks. When practicing, make sure to find a few questions on using the Goodness of Fit test on Normal or Binomial Distributions, as well as practice all 4 types of t-tests.
HL students, you'll also need to know things like the z-test (and when to use z or t-tests), Type I and Type II errors, critical regions, and generally making hypotheses for tests you've not seen before and drawing the right conclusions based on the values given. Let me know if you'd like a summary sheet on these!