Coverage adjustment is a statistical technique used to correct for biases or inaccuracies in data collection, ensuring that the sample accurately represents the entire population. It is crucial in surveys and censuses to account for undercoverage or overcoverage, enhancing the reliability and validity of the results.

Coverage Adjustment

Statistical technique

Biases in data collection

Sample accurately represents the entire population

Surveys and censuses

Undercoverage occurs when some members of the population are inadequately represented in a sample, leading to biased results and potentially flawed conclusions. This sampling error can significantly affect the validity of research findings, particularly in surveys and studies where inclusivity and comprehensive representation are crucial.

Undercoverage

Overcoverage occurs when a sampling frame includes elements not belonging to the target population, potentially leading to biased results. It is crucial to address overcoverage to ensure the validity and accuracy of statistical inferences drawn from the data.

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