What is undercoverage in statistics?

Undercoverage in statistics refers to when certain groups or individuals within a population are not adequately represented in a sample or survey. This can occur for various reasons, such as sampling bias, non-response bias, or limitations in data collection methods.

Undercoverage can lead to biased or inaccurate results because the sample may not accurately reflect the true characteristics of the population. For example, if certain demographic groups are underrepresented in a survey, the findings may not be generalizable or applicable to the overall population.

To address undercoverage, statisticians may use various techniques such as stratified sampling, oversampling of underrepresented groups, or adjusting the results through statistical weighting. It is important to be aware of the potential for undercoverage in any statistical analysis and take steps to minimize its impact on the results.