Final answer:
The level of significance refers to the probability of a Type I error, not a Type II error. The significance level (alpha) is the threshold for rejecting a true null hypothesis, while a Type II error (beta) occurs when a false null hypothesis is not rejected.
Step-by-step explanation:
The statement that the level of significance refers to the probability of making a Type II error is false. The level of significance (often denoted as α or alpha) is the threshold for the probability of making a Type I error, which occurs when the null hypothesis is true but is incorrectly rejected. Essentially, the level of significance defines how willing we are to risk making a Type I error. Conversely, a Type II error, denoted by β or beta, is the error of failing to reject a false null hypothesis.
When a hypothesis test is conducted at a 5 percent significance level, and we decide to reject or fail to reject the null hypothesis, we're operating within the pre-established alpha risk of 0.05 for committing a Type I error. The power of the test, which is 1 - β, quantifies the test's ability to correctly detect a true alternative when the null hypothesis is indeed false, hence the desire for minimizing both α and β to reduce the overall likelihood of errors.