The question is about interpreting a confidence interval. The correct interpretation of the given confidence interval is:

"We are 95% confident that the mean number of people in the 100,000 households is between 2.16 and 2.44."

Explanation:

1. Confidence intervals refer to a range of values where we estimate the true population parameter (in this case, the mean number of people per household in the population of 100,000 households) to lie with a certain level of confidence (95% in this case).
2. The sample of 625 households provides an estimate of the population mean, but the interval accounts for sampling variability.
3. Key point: The interpretation applies to the population (100,000 households), not the sample itself, and does not refer to individual probabilities or specific percentages of the sample.

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Related Questions:

1. What would happen to the confidence interval if the sample size were increased?
2. How would the interpretation change for a 99% confidence interval instead of 95%?
3. Why is it incorrect to say the probability is 0.95 for the mean to be in this interval?
4. What assumptions are required for the calculation of this confidence interval to be valid?
5. How is the margin of error calculated for this confidence interval?

Tip: Always remember, confidence intervals provide a range for population parameters, not probabilities about the sample or the data itself.