A study emphasizes that sample size affects reliability. Which inference is most consistent with this claim?

A study emphasizes that sample size affects reliability. Which inference is most consistent with this claim?

• A. Smaller samples always yield more reliable results
• B. Larger samples generally provide more reliable results
• C. Sample size has no impact on reliability
• D. Reliability depends only on measurement precision

Answer: (C)

Larger samples generally provide more reliable results because increasing the sample size reduces sampling error. When you take more observations, the estimate you compute (like a mean or proportion) tends to be closer to the true population value, and the variability of that estimate from one sample to another shrinks. This happens because the standard error of the estimate falls as 1 over the square root of the sample size, so confidence intervals become narrower and results are more stable across samples. The study’s claim that sample size affects reliability is about this direct relationship: more data points means less random fluctuation and more trustworthy conclusions. Smaller samples do not inherently yield more reliable results, and reliability isn’t determined solely by how precisely you measure something. Measurement precision matters, but even with precise measurements, a very small sample can produce unreliable estimates due to high sampling variability.

Larger samples generally provide more reliable results because increasing the sample size reduces sampling error. When you take more observations, the estimate you compute (like a mean or proportion) tends to be closer to the true population value, and the variability of that estimate from one sample to another shrinks. This happens because the standard error of the estimate falls as 1 over the square root of the sample size, so confidence intervals become narrower and results are more stable across samples. The study’s claim that sample size affects reliability is about this direct relationship: more data points means less random fluctuation and more trustworthy conclusions.

Smaller samples do not inherently yield more reliable results, and reliability isn’t determined solely by how precisely you measure something. Measurement precision matters, but even with precise measurements, a very small sample can produce unreliable estimates due to high sampling variability.