Asúlyozás

Asúlyozás is a process of assigning weights to elements to reflect their relative importance, frequency, or reliability. It is used in statistics, data analysis, decision making, and data processing to ensure that certain observations, criteria, or features contribute more or less to a result than others.

In statistics and survey methodology, weighting adjusts for unequal selection probabilities and response rates, producing estimates that better represent the target population. Different types of weights exist, such as sampling weights, frequency weights and analytic weights, each serving distinct purposes in calculations and models. In regression analysis, weighted least squares uses weights typically proportional to the inverse variance of observations, giving more reliable data points greater influence on the estimated relationships.

In meta-analysis, inverse-variance weighting combines study estimates to obtain a pooled effect size, with more precise studies contributing more to the overall result. In decision analysis and multi-criteria decision analysis, weights reflect the relative importance of criteria, guiding the aggregation of alternatives. In machine learning and data fusion, feature weighting and instance weighting adjust the influence of variables or samples during model training.

Weights can be determined a priori from subject-mmatter expertise or empirically using data-driven methods, such as inverse-variance, entropy-based approaches, or optimization techniques. Normalization is common, often scaling weights to sum to one or to the sample size to facilitate interpretation and comparison.

Caution is warranted because the choice of weights can affect conclusions and introduce bias if not justified or tested. Sensitivity analyses and transparent documentation are advisable to assess robustness.