Subsamples

A subsample is a subset drawn from a larger dataset or from a population, used for analysis when the full data are unavailable, too costly to process, or when evaluating variability and robustness. Subsamples can be obtained directly from the population or derived from an existing sample. They are distinct from the full dataset in that they contain only a portion of observations.

Subsampling can be performed in several ways. Simple random subsampling selects a subset at random without replacement. Stratified subsampling divides the population into subgroups (strata) and draws samples within each stratum to preserve the overall composition. Systematic subsampling chooses every k-th observation in a fixed order, often yielding practical convenience. Cluster subsampling draws entire clusters (groups of observations) and uses all units within the chosen clusters.

In resampling and model assessment, subsampling refers to generating samples of a smaller size m < n, typically without replacement, to estimate sampling distributions or to validate models. This contrasts with the bootstrap, where samples are drawn with replacement from the original data to form bootstrap replications. Subsampling-based methods can be advantageous when data do not meet bootstrap assumptions or when faster computation is required.

Applications of subsamples include exploratory data analysis, cross-validation, variance estimation, robust statistics, and privacy-preserving data practices, where only a portion of data is used. Key considerations include choosing an appropriate subsample size, ensuring representativeness, avoiding bias in the selection process, and adjusting analyses to account for reduced sample size and potential loss of information.