Students learn how to conduct hypothesis tests with the normal distribution, focusing on Type I and Type II errors. They calculate critical regions, determine Type I error probabilities, and apply significance levels to real-world scenarios.
As learning progresses, students calculate Type II error probabilities by assessing the likelihood of accepting a false null hypothesis.



