Chartered Financial Analyst (CFA) Practice Exam Level 2

What signifies a Type 2 error in statistical testing?

1. Failure to reject a true null hypothesis

2. Rejecting a false null hypothesis

3. Incorrectly accepting an alternative hypothesis

4. Announcing a significant result when it is not

The correct answer is: Failure to reject a true null hypothesis

A Type 2 error occurs when the null hypothesis is false but is not rejected. This error signifies a failure to detect an effect or difference when one truly exists. In statistical hypothesis testing, we start with a null hypothesis, which represents a default or status quo assumption. If the null hypothesis is true, the appropriate decision is to accept it. However, if the null hypothesis is indeed false and we fail to reject it, this misstep leads to a Type 2 error. The choices related to Type 2 errors clarify common misunderstandings in hypothesis testing. Accepting the null hypothesis when it is true is not a mistake; it is the correct decision at that point. Conversely, announcing a significant result when it is not refers to a Type 1 error, which misstates the status of the null hypothesis when it is incorrectly rejected. Thus, acknowledging the distinction between these concepts is vital for understanding hypothesis testing effectively.