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
Evaluation of SampleReduktion relies on assessing bias, variance, and predictive performance on held-out data. Simulation and
Applications include survey sampling to lower respondent burden, environmental monitoring with limited field measurements, clinical trials
See also: sampling, stratified sampling, cluster sampling, downsampling, undersampling, core-set, experimental design, survey methodology.