Setiny

Setiny is a term used in data science to describe a small, representative subset of a larger dataset chosen for preliminary analysis or testing.

Origin and usage: The word combines “set” and “tiny,” and it has appeared in online discussions about efficient data sampling in the mid-2010s. Since then, it has gained modest usage in tutorials and notebooks as a descriptive label for compact data samples used to prototype ideas or validate workflows before working with full datasets.

Definition and selection criteria: A setiny S subset D where |S| = k, with k much smaller than |D|. A setiny is considered effective if it preserves certain statistics of interest within defined tolerances. Common strategies include random sampling with stratification, reservoir sampling, or optimization-based selection to preserve distributions of target variables, such as means, variances, or class ratios.

Applications: Setinies are used for rapid exploratory analysis, method prototyping, and teaching concepts of sampling and bias. They help teams sanity-check data processing pipelines, feature engineering ideas, or model ideas before scaling experiments to the full dataset.

Limitations: Setinies may introduce sampling bias if the selection method is inappropriate or if rare events are underrepresented. Results are approximate and depend on the sampling method and randomness. Reproducibility requires fixed random seeds or a clearly documented selection procedure and parameters.

See also: sample, stratified sampling, pilot study, subset.