In general, as the sample size (n) increases...

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The confidence interval is unaffected

The confidence interval becomes less accurate

The confidence interval gets narrower

The confidence interval gets wider

The correct choice is:

**The confidence interval gets narrower.**

As the sample size (n) increases, the standard error decreases, leading to a narrower confidence interval. This means that the estimate becomes more precise, giving a better idea of the true population parameter.

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Here are five related questions for further exploration:

1. How does the standard error relate to the confidence interval?
2. What factors other than sample size can influence the width of a confidence interval?
3. How does the confidence level (e.g., 95%) affect the confidence interval?
4. What is the impact of variability in the data on the confidence interval?
5. How does increasing the sample size affect the margin of error in a confidence interval?

**Tip:** Increasing the sample size improves the precision of estimates but be mindful of diminishing returns, as very large samples can be costly and may not yield proportionally narrower confidence intervals.

Explore how sample size affects confidence intervals in statistics. Learn why as the sample size (n) increases, the confidence interval gets narrower, improving precision in estimating population parameters. Discover key formulas like the Standard Error formula and concepts such as the Central Limit Theorem.

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