Final answer:
Rejecting a false null hypothesis is a correct decision in hypothesis testing, avoiding both Type I and Type II errors. Option B is the correct answer.
Step-by-step explanation:
When conducting hypothesis tests, it is crucial to understand the outcomes related to the null hypothesis. There are two types of errors to be aware of: Type I error and Type II error. A Type I error occurs when the null hypothesis is true, but we incorrectly reject it. Conversely, a Type II error happens when the null hypothesis is false, and we fail to reject it.
In the scenario where we reject the null hypothesis and it is actually false, we have not committed a Type I error, as this error only occurs when the null hypothesis is true. Similarly, we have not committed a Type II error because this error only arises when we fail to reject a false null hypothesis. Thus, rejecting a false null hypothesis means we have made a correct decision.
To summarize, when we reject the null hypothesis when it is actually false, this is the desired outcome in hypothesis testing, meaning we have correctly identified that an effect exists or that there is a significant difference, avoiding both Type I and Type II errors.