misweighted

Misweighted is an adjective describing a situation in which weights assigned to observations, features, or outcomes do not correctly reflect their intended importance, frequency, or probability. In statistics and data analysis, misweighted data can lead to biased estimates, inaccurate uncertainty, or degraded predictive performance. The problem can occur in survey sampling when weights do not match the true population proportions, in machine learning when class or sample weights are chosen incorrectly, or in importance sampling when weights fail to approximate the target distribution.

Causes include errors in weight calculation, inappropriate weighting schemes, missing data treated improperly, or changes in the underlying distribution that are not accounted for. Indicators of misweighting can be systematic bias in estimates, an inflated or reduced effective sample size, or deteriorated validation metrics when weights are applied.

Effects of misweighting include biased parameter estimates, biased predictions, poor calibration, and incorrect uncertainty quantification. Inaccurate weights can also distort the perceived significance of features or interventions and mislead decision making.

Detection and correction involve auditing the weighting scheme, verifying normalization (ensuring weights sum to the intended total), and reweighting data with correct probabilities. In machine learning, alternative weighting strategies, stratified sampling, or distribution-matching reweighting can be considered. Sensitivity analysis and cross-validation help assess robustness to misweighting.

Related concepts include sample weighting, probability weighting, importance sampling, and survey weights. While not a formal, universally standardized term, misweighted data are routinely addressed in statistical practice to restore accuracy and reliability.