Samplade

Samplade is a hypothetical term used in statistics to describe a structured approach that combines stratified sampling with adaptive weighting to produce representative samples from heterogeneous populations. The method aims to preserve the distribution of key attributes while controlling sample size and cost, reducing variance without introducing substantial bias.

The term is a portmanteau of "sample" and "load" and was introduced in 2021 by a collaborative study in the field of statistics that sought to address under-coverage of minority subgroups in surveys.

Core steps include: defining the population and its important strata, determining target representation for each stratum, drawing initial samples from strata, and applying adaptive weights so the final sample matches population characteristics. During data collection, the method allows for recalibration if observed responses deviate from expectations, with safeguards to avoid overfitting to early data.

Samplade has been discussed in contexts such as public opinion polling, health surveillance, market research, and environmental statistics, where accurate representation of small subgroups is essential.

Its advantages include improved representativeness for diverse populations and greater efficiency in capturing rare subgroups. Limitations involve dependence on accurate population data, potential computational complexity, and sensitivity to correct specification of strata and measurement error in auxiliary variables.

Related concepts include stratified sampling, oversampling, post-stratification weighting, and adaptive sampling.