The correct answer is:

A.
The 95% represents the proportion of intervals that would contain the parameter (for example, the population mean or population proportion) if a large number of different samples is obtained.

Explanation:

A 95% confidence interval means that if we were to repeatedly draw random samples from the population and calculate a confidence interval for each sample, approximately 95% of those intervals would contain the true population parameter. This does not mean there's a 95% chance that a single calculated interval contains the true parameter—it is about the long-term frequency of capturing the parameter across many samples.

Do you want more details or have any questions?

Here are 5 related questions to expand your understanding:

1. How do you calculate a confidence interval for the mean of a population?
2. What is the difference between confidence intervals and prediction intervals?
3. How does increasing the confidence level (e.g., from 95% to 99%) affect the width of the confidence interval?
4. What assumptions are required to construct a valid confidence interval?
5. How does sample size influence the accuracy of a confidence interval?

Tip: Confidence intervals provide an estimate of uncertainty. A narrower interval indicates more precision, but achieving it often requires larger sample sizes.