Final answer:
Decreasing alpha from 0.10 to 0.01 makes it more difficult to reject the null hypothesis and decreases the probability of making a Type I error.
Step-by-step explanation:
When considering hypothesis testing, if we decrease the significance level (alpha) from 0.10 to 0.01, we make it more difficult to reject the null hypothesis. This is because a smaller alpha means a more stringent criterion for rejecting the null hypothesis, which directly founds our conclusion on stronger evidence. At the same time, a lower alpha decreases our probability of making a Type I error, which occurs when we falsely reject a true null hypothesis. Therefore, the answer to the student's question is 'a. more; decrease'.
For example, if we conducted an independent-samples t-test with a given alpha, we will only reject the null hypothesis if the p-value is less than the chosen alpha level. With an alpha at 0.01, it becomes harder to find enough evidence against the null hypothesis, and consequently, the probability of committing a Type I error is reduced.