Sampling error primarily occurs when:

Sampling error primarily occurs when the sample does not accurately represent the population. This concept is rooted in the principle that a sample should reflect the characteristics of the larger group from which it is drawn. If a sample is biased or unrepresentative, it can lead to incorrect conclusions about the population, such as misestimating population parameters or failing to identify trends and patterns that exist within the population as a whole.

For example, if a survey about community health is conducted only among a specific socioeconomic group, the findings may not be generalizable to the entire community. In contrast, using a sample that adequately captures the diversity of the population will yield more reliable and valid results. Consequently, recognizing and minimizing sampling error is crucial in research to ensure that findings are applicable to the population being studied.