Final answer:
A Type II error occurs when the null hypothesis is not rejected despite being false. This error is a result of insufficient evidence and affects the statistical test's power. It is not to be confused with a Type I error, which is rejecting a true null hypothesis.option b is correct.
Step-by-step explanation:
When a researcher fails to reject the null hypothesis when it is, in fact, false, the error being made is known as a Type II error. This type of error occurs because the test did not have enough evidence, or power, to demonstrate that the null hypothesis was false. An example of this would be not rejecting the null hypothesis that the percentage of adults who have jobs is at least 88 percent when in reality that percentage is less than 88 percent. Another way to understand this error is by considering the consequences in a medical scenario where a Type II error may result in concluding that a patient is not sick when they actually are.
To compare, a Type I error involves rejecting a true null hypothesis while a Type II error involves failing to reject a false null hypothesis. The probability of making a Type II error is denoted by Beta (�) and is related to the concept of statistical power, which is the probability of correctly rejecting a false null hypothesis.