Final answer:
A Type I error happens when the null hypothesis is rejected incorrectly, while a Type II error occurs when a false null hypothesis is not rejected. These errors reflect false positive and false negative outcomes in hypothesis testing.
Step-by-step explanation:
A Type I error occurs when the null hypothesis is incorrectly rejected, meaning that a decision is made to reject the null hypothesis when, in fact, it is true. This specific error represents a false positive in the context of hypothesis testing. For instance, if it is claimed that the proportion of first-time brides who are younger than their grooms is different from 50 percent when it's 50 percent, and we reject the null hypothesis (suggesting that there is a difference), that would be a Type I error. Type II error, on the other hand, occurs when one fails to reject a null hypothesis that is false, which is a false negative situation. In our previous example, if the proportion of brides who are younger than their grooms does differ from 50 percent, but the null hypothesis is not rejected, that would be a Type II error.