Final answer:
A Type II error is made if you fail to reject the null hypothesis when it is actually false. It occurs when there is insufficient evidence to prove the null hypothesis wrong, despite its falseness.
Step-by-step explanation:
If you fail to reject the null hypothesis when the null hypothesis is actually false, this is known as a Type II error. The Type II error occurs because there is not enough evidence in your sample to conclude that the null hypothesis is incorrect, despite it being false in reality.
In hypothesis testing, Type I error and Type II error are possible incorrect decisions. A Type I error happens when you reject a true null hypothesis, while a Type II error is when you do not reject a false null hypothesis. Both of these errors have associated probabilities, denoted by alpha (α) for Type I error and beta (β) for Type II error.
For example, if the null hypothesis is that the percentage of adults who have jobs is at least 88 percent and the actual percentage is less than 88 percent, not rejecting this null hypothesis would constitute a Type II error.