SampleReduktion

SampleReduktion refers to techniques for reducing the number of samples collected or used in analysis with the aim of lowering cost, storage, or processing time while attempting to preserve accuracy and representativeness. It is relevant in statistics, data science, survey design, and experimental research, as well as in signal processing where samples may be downsampled in time or space.

Common approaches include random subsampling, systematic sampling, stratified sampling, and cluster sampling. In data analytics, downsampling and undersampling are frequently used, while core-set selection seeks a small, informative subset that approximates the full dataset. The choice of method depends on the objective, the population structure, and the acceptable level of error or bias. Sample size can be determined using formulas for margin of error, cost constraints, or optimization against a predefined loss.

Evaluation of SampleReduktion relies on assessing bias, variance, and predictive performance on held-out data. Simulation and resampling experiments (e.g., bootstrap, cross-validation) help quantify uncertainty introduced by reduction. Documentation should include the reduction method, the assumed population characteristics, and the estimated impact on results.

Applications include survey sampling to lower respondent burden, environmental monitoring with limited field measurements, clinical trials with early stopping or interim analyses, and machine learning workflows where a compact core-set accelerates training while preserving model accuracy. Limitations include potential bias if the sampling frame is incomplete or if dependence among samples is not accounted for, as well as the risk of overestimating performance from overly optimistic validation.

See also: sampling, stratified sampling, cluster sampling, downsampling, undersampling, core-set, experimental design, survey methodology.