1. Explain the difference between a Type I and Type II error.
2. Explain which corresponds to α (alpha) and β (Beta). 3. Explain which becomes smaller the more concerned we are about making a Type I error?
Instructions
Scenario: You are now known as the class’ statistics tutor. Your professor did a poor job of covering Type I and Type II errors, and it falls to you to explain what these terms mean.
Prompt: Your friends have five questions:
1. What is a Type I error? 2. What is a Type II error? 3. What is the relation between these two kinds of errors? 4. Which corresponds to α (alpha) and β (Beta)? 5. Why do we bother with this? More precisely, how does all of this help us decide where to set our rejection level when hypothesis testing?
They would love it if you can explain this using a story, scenario or even a table.
Although your friends have read Rogerson Statistical Methods for Geography, Chapter 5, they are still confused. If you can’t find your copy of Rogerson, the Sakai site has a link to an electronic copy of the first edition. There may be better explanations, too, in other sources.