Why are different sampling methods likely to result in different misstatements?

Sampling methods can yield different misstatements primarily due to how they interact with the variability inherent in the population from which samples are drawn. When a population has a high degree of variability—meaning that there are significant differences in the characteristics or performance of individual items—certain sampling methods might capture that variability better than others.

For instance, some methods may randomly select items without regard for their distribution within the population, potentially leading to a sample that does not accurately represent the variety present, thus skewing results. In contrast, stratified sampling techniques intentionally account for different subgroups within the population, which can provide a more accurate representation and therefore different estimates of misstatements.

Additionally, the sampling method chosen can influence the degree to which certain types of misstatements are captured or overlooked. For example, if misstatements are concentrated in a specific area of the population, a random sampling method might miss them while a targeted approach would identify them more readily.

This dynamic illustrates why understanding the population's variability and how it influences each sampling method's effectiveness is crucial in audit sampling.