What term describes a sampling method that leads to an unrepresentative sample?

Sampling bias refers to a systematic error that results when a sample does not accurately represent the population from which it is drawn. This occurs when certain members of the population are more likely to be selected than others, often due to the method used to select the sample. For example, if researchers conduct a survey exclusively in a particular geographic area, they might inadvertently exclude perspectives from other regions, leading to an unrepresentative sample.

Understanding sampling bias is crucial because it can skew results and lead to conclusions that do not accurately reflect the broader population. In research, addressing potential sources of bias is essential to improving the validity and reliability of findings. In contrast, sampling error refers to the natural variability that occurs by chance when selecting a sample, which isn’t technically a form of bias. Statistical significance pertains to the likelihood that results are not due to chance, and regression toward the mean refers to the phenomenon where extreme observations tend to move closer to the average on subsequent measurements.