The correct interpretation of the P-value is:

"Assuming 65% of households would support starting school earlier, there is a 0.29 probability of getting a sample proportion of 0.70 or more different from 0.65."

This is because a P-value measures the probability of observing a result as extreme or more extreme than the one found in the sample, assuming that the null hypothesis (in this case, that 65% of households still support starting school earlier) is true.

To clarify:

• The null hypothesis is that the true proportion of households that support starting school earlier is still 65%.
• A P-value of 0.29 indicates that there is a 29% chance of getting a sample proportion of 70% (0.70) or more different from 65% (0.65) if the true proportion is indeed 65%.
• Since this P-value is relatively large, it suggests that there is no strong evidence to reject the null hypothesis.

Would you like more details on hypothesis testing or the interpretation of P-values?

Here are 5 questions related to this topic:

1. What are the null and alternative hypotheses in hypothesis testing?
2. How do you calculate a P-value for a proportion hypothesis test?
3. What is the significance level, and how does it relate to the P-value?
4. What does it mean if the P-value is smaller than the significance level?
5. How would you compute a confidence interval for the proportion of households supporting starting school earlier?

Tip: A P-value only tells you the probability under the null hypothesis, not whether the null hypothesis is true.