Final answer:
Type I error occurs when a true null hypothesis is rejected, while Type II error occurs when a false null hypothesis is not rejected. Type II error is more serious as it means ineffective treatment will not be identified.
Step-by-step explanation:
Type I error occurs when a true null hypothesis is rejected, meaning that we conclude that there is a significant difference or effect when there is actually none. An example is rejecting the null hypothesis that the proportion of first-time brides who are younger than their grooms is 50 percent when it is actually 50 percent. Type II error occurs when a false null hypothesis is not rejected, meaning that we fail to identify a significant difference or effect when there is actually one. An example is not rejecting the null hypothesis that a certain drug has a cure rate of at least 75 percent for males with a disease, even though it has a cure rate lower than 75 percent. In this case, Type II error is more serious as it means ineffective treatment will not be identified and patients will not receive the necessary treatment.