Weighting adjustment is a post-collection statistical process that corrects for sample imbalances by assigning multipliers to individual responses, ensuring the final dataset reflects the true composition of the target population. In quantitative research, even carefully recruited samples can skew by age, gender, region, or other demographic variables, and weighting adjustment corrects for that drift before analysis begins. Researchers apply weighting schemes such as rim weighting or cell weighting to align sample proportions with known population benchmarks, typically drawn from census data or verified audience profiles. Without weighting adjustment, findings can misrepresent the population and lead to decisions based on structurally biased data.