Answer: Type I error and Type II error are associated with hypothesis testing, where we test a hypothesis by collecting data and analyzing it.
For the given hypothesis, we can set up the null hypothesis as follows:
H0: The percentage of households with Internet access is less than or equal to 60%.
And the alternative hypothesis as:
Ha: The percentage of households with Internet access is greater than 60%.
Now, a Type I error occurs when we reject the null hypothesis (i.e., conclude that the percentage of households with Internet access is greater than 60%) when it is actually true. This means that we would be making a false claim that the percentage of households with Internet access is greater than 60%, when it is not.
On the other hand, a Type II error occurs when we fail to reject the null hypothesis (i.e., conclude that the percentage of households with Internet access is less than or equal to 60%) when it is actually false. This means that we would be missing the truth that the percentage of households with Internet access is greater than 60%.
So, in the context of the given hypothesis, a Type I error would be to conclude that the percentage of households with Internet access is greater than 60% when it is actually less than or equal to 60%, and a Type II error would be to fail to conclude that the percentage of households with Internet access is greater than 60% when it is actually greater than 60%.