Which scenario describes a Type II Error?

A Type II Error occurs when a statistical test fails to reject a null hypothesis that is actually false. This means that the test concludes there is not enough evidence to support an alternative hypothesis when, in reality, there is. In simpler terms, a Type II Error leads to a missed opportunity to identify a real effect or relationship that exists.

In the context of the question, the correct answer describes this scenario accurately. When you fail to reject a true null hypothesis, it implies that you are accepting the null hypothesis even though the evidence suggests it should be rejected. This is quintessentially the essence of a Type II Error.

The other scenarios provided do not correctly represent a Type II Error. For instance, rejecting a true null hypothesis refers to a Type I Error, where a false positive outcome occurs. Similarly, incorrectly confirming a hypothesis could lead to confusion but does not specifically define the Type II scenario. Thus, only the situation described in the correct answer aligns with the definition of a Type II Error.