For a null hypothesis , a type I error rejects when it is true, while a type II error does not reject when the alternative is true. If a test does not reject on an event , their conditional probabilities are and . Nonrejection does not establish the truth of the null hypothesis.
Articles by others on the same topic
Type I and Type II errors are concepts in statistics that describe the potential errors that can occur when testing a hypothesis. 1. **Type I Error (False Positive)**: This occurs when a null hypothesis (H0) is rejected when it is actually true. In simpler terms, it means that the test indicates a significant effect or difference when there actually is none.