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 preserve feature distributions. The performance of a model is assessed on the held-out portion rather than the data used to fit the model. This approach helps estimate generalization error and prevents overfitting. Related practices include cross-validation and bootstrapping, which use repeated splits to obtain more stable performance estimates.

In laboratory or clinical contexts, a physical sample can be split into parts to perform parallel assays, enabling comparison of methods, controls, or reagents. Splitting supports replication and quality control, and can help manage limited material by allocating portions to different analyses.

Key considerations when applying a splitsample approach include ensuring representative and random splitting, avoiding data leakage between splits, and maintaining sufficient size in each sub-sample to preserve statistical power or assay reliability. The terminology may vary by discipline, with some literature preferring split sample, split-sample, or data partitioning.

See also: data splitting, cross-validation, train-test split, replication, experimental design.