Final answer:
A Type I error occurs when the null hypothesis is incorrectly rejected, believing there is an effect or difference when there isn't one, known as a false positive. In hypothesis testing, the probability of this error is denoted by α, the level of significance.
Step-by-step explanation:
Definition of Type I Error
The definition of a Type I error is making the incorrect decision to reject the null hypothesis (H0) when, in fact, the null hypothesis is true. This can be thought of as a 'false positive,' where an effect or difference is detected when there is none. In hypothesis testing, the probability of a Type I error occurring is represented by the Greek letter α and is also known as the level of significance of the test. Hence, the correct answer to the student's question is b) Rejecting the null hypothesis when the null hypothesis is really true.
Examples of Outcomes
- If you perform a test to determine if a rock climbing equipment is safe (H0: equipment is safe), and you incorrectly conclude that it's unsafe, you have made a Type I error.
- If a medical test suggests a patient has a disease when they actually do not, that's a Type I error.