An example of a sample that is too small would be:

In statistical analysis, the size of a sample can significantly affect the reliability of the results. Option A describes 100 completed surveys from a population of 20,000. This represents only 0.5% of the population, which is quite small considering the sheer size of the population. A sample that constitutes just a fraction of 1% may not capture the variability and characteristics present in the entire population, leading to skewed or unreliable results.

Using a small sample like this can introduce a high margin of error and limit the ability to generalize findings to the broader population. In contrast, a larger sample size tends to have better representation since it encompasses more diverse perspectives and experiences, thus providing more accurate insights into the population under study.

The other options either depict larger samples relative to their respective populations or focus on survey characteristics rather than the sample size in relation to the population. This highlights the importance of having an adequately sized sample to ensure that findings are robust and can serve as a sound basis for decision-making.