For a null hypothesis , a Type I error rejects when it is true, while a Type II error accepts when the alternative is true. If a test accepts on an event , their conditional probabilities are and .
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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.