Final answer:
Sampling error is the difference between a numerical description of the population and the corresponding descriptor of the sample. It is the natural variation that arises when a sample represents a larger population and can be decreased by using larger sample sizes. Sampling error is distinguished from nonsampling error, which stems from human errors and other factors unrelated to the sampling process.
Step-by-step explanation:
The sampling error is the difference between a numerical description of the population and the corresponding descriptor of the sample. This error is the natural variation that results from selecting a sample to represent a larger population. Sampling error can be affected by the sample size; a larger sample size tends to reduce the error. It is important to differentiate sampling error from nonsampling error, which is an issue that affects the reliability of sampling data due to factors other than natural variation, such as human errors in study design or data collection.
When conducting an observational study, researchers must take care to ensure a representative sample to minimize sampling bias, which occurs when not all members of the population are equally likely to be selected. Understanding and minimizing both sampling error and nonsampling error is critical to improve the reliability and validity of the study results.