Final answer:
When the percentage of a Type I error increases, it typically entails a lower threshold for rejecting the null hypothesis, which corresponds to a decrease in the likelihood of a Type II error.
Step-by-step explanation:
In statistics, when the percentage of a Type I error increases, it generally means that the threshold for rejecting the null hypothesis has been lowered. A Type I error occurs when the null hypothesis is true but is incorrectly rejected. Conversely, a Type II error occurs when the null hypothesis is false but is erroneously not rejected. These two types of errors are inversely related; as the probability of committing a Type I error (denoted by α) increases, the probability of committing a Type II error (denoted by β) typically decreases. The reason for this inverse relationship is that by being more willing to reject the null hypothesis (higher α), we also become less likely to fail to reject a false null hypothesis (lower β).
The correct answer to your question from the given options is b - The percentage will decrease.