1. The problem asks to explain Type I and Type II errors in hypothesis testing. 2. A Type I error occurs when we reject the null hypothesis $H_0$ even though it is true. It is a false positive. 3. A Type II error occurs when we fail to reject the null hypothesis $H_0$ even though the alternative hypothesis $H_a$ is true. It is a false negative. 4. For the hypothesis test in Exercise 4, where $H_0$: the percentage of voters who believe they voted for the winning candidate is 43%, and $H_a$: the percentage is not 43%: 5. A Type I error would be concluding that the percentage is different from 43% when in fact it is exactly 43%. 6. A Type II error would be concluding that the percentage is 43% when in fact it is different from 43%.