Increasing the significance level would increase the probability of a Type I error.

Increasing the significance level would increase the probability of a Type I error.

• A. True
• B. False
• C. It depends
• D. Not sure

Answer: (A)

The key idea is that the significance level (alpha) sets the chance we’re willing to take to make a false claim. When the null hypothesis is actually true, the probability of rejecting it—i.e., making a Type I error—equals the chosen alpha. If you raise alpha, you loosen the criteria for claiming a finding, so you’ll reject the null more often even when it’s true. In practical terms, decisions are made by comparing the p-value to alpha: reject if p-value is at most alpha. A higher alpha means more p-values will meet that threshold, increasing the chance of a false positive. So increasing the significance level does increase the probability of a Type I error. This reflects the trade-off between detecting real effects and avoiding false alarms.

The key idea is that the significance level (alpha) sets the chance we’re willing to take to make a false claim. When the null hypothesis is actually true, the probability of rejecting it—i.e., making a Type I error—equals the chosen alpha. If you raise alpha, you loosen the criteria for claiming a finding, so you’ll reject the null more often even when it’s true. In practical terms, decisions are made by comparing the p-value to alpha: reject if p-value is at most alpha. A higher alpha means more p-values will meet that threshold, increasing the chance of a false positive. So increasing the significance level does increase the probability of a Type I error. This reflects the trade-off between detecting real effects and avoiding false alarms.