What is Sample Weighting?
Sample weighting adjusts survey results so the responding group better matches the population you care about. It gives more or less influence to individual responses to correct for known differences between your sample and the target population.
Sample weighting is a statistical technique used to make survey estimates more representative of a target population. When some groups are over- or under-represented among respondents (for example, more English speakers answered than non-English speakers), a weight is assigned to each response so aggregated results reflect the known distribution of characteristics in the full population. Common approaches include simple ratio weights (population proportion divided by sample proportion), post-stratification (weighting across a few demographic cells), raking (iterative adjustment to match marginal distributions), and propensity weighting (model-based adjustment for response likelihood).
Weights are calculated from known population benchmarks (census counts, school registers, voter rolls, or administrative data) and applied when computing totals, means, percentages and statistical estimates. Weighting corrects for unequal selection probabilities and some forms of nonresponse bias, but it does not fix measurement error, poorly worded questions, or unknown biases for which you have no benchmarks.
Usage example
A local council surveys residents about bin collection. The council knows its population is 30% households where English is not the main language, but only 15% of survey responses came from those households. To compensate, the council applies weights: weight_non-English = 0.30 / 0.15 = 2.0 and weight_English = 0.70 / 0.85 ≈ 0.82. When computing overall satisfaction rates, each non-English response counts twice and each English response counts ~0.82 times.
Practical application
Weighting matters because raw respondent samples often differ from the groups decision-makers need to hear from. Applying appropriate weights produces estimates that better reflect the whole community — for example, estimating true support for a policy across all language groups rather than only the subgroup that was more likely to respond. In multilingual projects, weighting helps correct participation imbalances between language communities when you have reliable population benchmarks for those languages. That said, weighting increases estimate variance (reduces effective sample size) and depends on accurate benchmarks; whenever possible, combine weighting with efforts to boost participation among underrepresented groups and report both weighted and unweighted results for transparency.
FAQ
How do I pick which variables to weight on?
Choose variables that (1) are linked to both response likelihood and the outcomes you measure, and (2) have reliable population benchmarks (e.g., age, gender, language, area). Avoid overcomplicating the weighting scheme with many small cells you can't populate from external data — that creates unstable weights. Start with a few key dimensions that matter for representativeness.
Will weighting change what individual respondents said?
No — weighting doesn't alter individual answers. It adjusts how much influence each response has when you compute aggregated results. A respondent's text answer or selection remains the same, but its contribution to the overall percentages or averages is scaled by the assigned weight.
Can weighting fix low response from a language community?
Weighting can reduce bias in aggregate estimates if you have accurate population benchmarks for that language group, but it can't replace real responses or fix problems like misunderstandings, poor translation, or measurement error. Where possible, prioritise improving outreach and survey experience to increase genuine participation from underrepresented language communities; use weighting as a corrective, not a complete substitute.