Final answer:
The statement is false because choosing a large significance level increases the probability of making a Type I error, which is not desirable even when a Type I error is considered less severe than a Type II error.
Step-by-step explanation:
The statement is false. In hypothesis testing, the significance level (denoted as α, alpha) represents the threshold for the probability of making a Type I error, which is rejecting a true null hypothesis. When we say that a Type I error is less severe than a Type II error, it does not mean that we should choose a large significance level like 0.9.
Instead, we typically choose a smaller significance level (e.g., 0.05) to reduce the likelihood of making a Type I error. However, the severity of Type I and Type II errors depends on the context of the research question, and we should adjust the significance level accordingly to balance the risks of both errors. A large significance level would increase the risk of incorrectly rejecting a true null hypothesis, which is undesirable.