To determine the correct answers, let's go through the definitions of Type I and Type II errors in the context of hypothesis testing.

1. Type I Error (False Positive): This error occurs when we reject the null hypothesis when it is actually true.

   • For this question, the null hypothesis states that the percentage of households with internet access is equal to 60%.
   • Thus, a Type I error would mean rejecting this hypothesis when the actual percentage is indeed 60%.

   Based on this, the correct answer for the Type I error is:

   • Option C: "Reject the null hypothesis that the percentage of households with internet access is equal to 60% when the percentage is actually equal to 60%."

2. Type II Error (False Negative): This error happens when we fail to reject the null hypothesis when it is actually false.

   • Here, failing to reject the hypothesis would mean believing the percentage is 60% when it is actually different from 60%.

   The correct answer for the Type II error is:

   • Option B: "Fail to reject the null hypothesis that the percentage of households with internet access is equal to 60% when that percentage is actually different from 60%."

Would you like more explanation on Type I and Type II errors or have any further questions?

Here are five related questions to expand your understanding:

1. What is the impact of sample size on Type I and Type II errors?
2. How does changing the significance level affect the likelihood of Type I and Type II errors?
3. Can you explain how Type I and Type II errors relate to confidence intervals?
4. What strategies can be used to reduce the chances of a Type II error?
5. How can hypothesis tests be applied in real-world scenarios outside of internet access?

Tip: Remember, lowering the chance of a Type I error usually increases the chance of a Type II error and vice versa. Balancing them is crucial in hypothesis testing.