What limitation is associated with sampling in terms of document counts?

Sampling is a statistical technique used to select a subset of items from a larger population to draw conclusions about that population. In the context of document counts, a key limitation of sampling is that it cannot provide an accurate representation of the population if the total number of documents is too small. Specifically, when the number of documents is less than a certain threshold—commonly accepted as around 300—the sample may not adequately reflect the diversity or characteristics of the entire set. This is due to the variability inherent in smaller groups, which can lead to skewed or biased results.

When sampling fewer than 300 documents, the likelihood of encountering extreme values or outliers increases, ultimately affecting the validity of the inferences drawn from the sample. A sample that is too small does not capture the full range of scenarios and conditions that would be present in a larger dataset, thus limiting the reliability of the conclusions one can make based on that sample.

In contrast, the other limitations related to document counts do not accurately encapsulate the inherent challenges of sampling. For instance, while some systems might limit the number of documents that can be sampled at a particular time, this is not a universal rule applied to all document sampling techniques. Additionally, sampling is not restricted to only documents marked