- Identify Type I and Type II errors
- Select an appropriate significance level (i.e., \(\alpha\) level) for a given scenario
- Explain the problems associated with conducting multiple tests
- Interpret the results of a hypothesis test in terms of practical significance
- Distinguish between practical significance and statistical significance
- Explain how changing different aspects of a research study would change the statistical power of the tests conducted
- Compare and contrast confidence intervals and hypothesis tests
Last week you learned how to conduct a hypothesis testing using randomization procedures in StatKey. This week we are going to delve a bit deeper into hypothesis testing. Concepts such as errors, significance (\(\alpha\)) levels, issues with multiple testing, practical significance, and statistical power apply to hypothesis tests for all the parameters that we have learned and will also apply to those that we will learn later in this course.