splitsample
Splitsample is a term used to describe the practice of dividing a single sample into multiple parts for separate analyses. The aim is to enable independent examination of each part, increase reliability through replication, or facilitate validation of results across subsamples. The exact meaning varies by field, but a common use in data analysis is to partition a dataset into disjoint sub-samples for training and validation in predictive modeling.
In statistics and machine learning, a split-sample design involves randomly splitting data, sometimes with stratification to
In laboratory or clinical contexts, a physical sample can be split into parts to perform parallel assays,
Key considerations when applying a splitsample approach include ensuring representative and random splitting, avoiding data leakage
See also: data splitting, cross-validation, train-test split, replication, experimental design.