Final answer:
A Type II error occurs when one fails to reject a false null hypothesis, believing it to be true when it is not. The correct answer is b. False, indicating that a Type II error is a false negative result in hypothesis testing.
Step-by-step explanation:
A Type II error occurs in the context of hypothesis testing in statistics. This error is made when the decision is not to reject the null hypothesis when, in fact, the null hypothesis is false. To clarify, the Type II error is a false negative result in hypothesis testing.
Contrastingly, a Type I error happens when one rejects a true null hypothesis, a false positive result. These errors are represented by the Greek letters α (alpha) for a Type I error and β (beta) for a Type II error. Minimizing both errors is crucial, although they can rarely be reduced to zero.
In the scenario provided, the null hypothesis would not be rejected erroneously believing it to be true when it is, indeed, false. Thus, the correct answer is b. False. It's important to remember that making a Type II error means failing to identify an actual effect or difference when one exists.