As the significance level decreases, what happens to Type I Error?

As the significance level decreases, what happens to Type I Error?

• A. Increases
• B. Decreases
• C. Remains the same
• D. Varies

Answer: (B)

When the significance level decreases, the probability of committing a Type I Error — which is the incorrect rejection of a true null hypothesis — also decreases. The significance level, commonly denoted as alpha (α), represents the threshold at which you are willing to reject the null hypothesis. If you set a lower alpha level, such as moving from 0.05 to 0.01, you are asserting that you require stronger evidence to reject the null hypothesis. This increased rigor means that only more substantial evidence against the null hypothesis will lead to a rejection. Consequently, as the significance level lowers, the chance of falsely identifying a significant effect when there is none (i.e., making a Type I Error) diminishes. Thus, a decrease in the significance level correlates directly with a reduction in the likelihood of Type I Errors, reinforcing the reasoning that a more stringent criterion decreases the probability of erroneously rejecting the null hypothesis.

When the significance level decreases, the probability of committing a Type I Error — which is the incorrect rejection of a true null hypothesis — also decreases. The significance level, commonly denoted as alpha (α), represents the threshold at which you are willing to reject the null hypothesis.

If you set a lower alpha level, such as moving from 0.05 to 0.01, you are asserting that you require stronger evidence to reject the null hypothesis. This increased rigor means that only more substantial evidence against the null hypothesis will lead to a rejection. Consequently, as the significance level lowers, the chance of falsely identifying a significant effect when there is none (i.e., making a Type I Error) diminishes.

Thus, a decrease in the significance level correlates directly with a reduction in the likelihood of Type I Errors, reinforcing the reasoning that a more stringent criterion decreases the probability of erroneously rejecting the null hypothesis.