What effect does a large, well-represented sample have on normative estimates?

What effect does a large, well-represented sample have on normative estimates?

• A. Larger samples provide more precise estimates and narrower confidence intervals.
• B. Larger samples decrease precision.
• C. Larger samples increase bias.
• D. Sample size has no effect on confidence intervals.

Answer: (A)

A large, well-represented sample makes normative estimates more precise because sampling error shrinks as more data are collected. As the sample size grows, the standard error decreases roughly with 1 over the square root of the sample size, so the margin of error around the estimate gets smaller and the confidence interval narrows. This means the computed normative value—for example, an average or percentile—better reflects the true population value. Having a larger, representative sample also helps avoid bias from unrepresentative data, but the key gain here is precision, not an increase in bias. The other ideas conflict with this relationship: increasing sample size does not reduce precision, and it does affect confidence intervals by making them narrower.

A large, well-represented sample makes normative estimates more precise because sampling error shrinks as more data are collected. As the sample size grows, the standard error decreases roughly with 1 over the square root of the sample size, so the margin of error around the estimate gets smaller and the confidence interval narrows. This means the computed normative value—for example, an average or percentile—better reflects the true population value. Having a larger, representative sample also helps avoid bias from unrepresentative data, but the key gain here is precision, not an increase in bias. The other ideas conflict with this relationship: increasing sample size does not reduce precision, and it does affect confidence intervals by making them narrower.