Stratified sampling

About

Stratified sampling is a statistical technique in which a population is divided into distinct subgroups, or strata, based on shared characteristics, and samples are then drawn independently from each subgroup. This ensures that every meaningful segment of the population is proportionally or deliberately represented in the final dataset, reducing sampling bias and improving the reliability of conclusions drawn from the data. In robotics and AI, stratified sampling appears in several important contexts: training data collection ensures that machine learning models are exposed to balanced examples across categories such as environments, object classes, or demographic groups; reinforcement learning uses stratified experience replay to maintain diverse scenario coverage during policy training; and multi-robot path planning research employs it to evaluate algorithm performance across varied configurations. It also underpins experimental design in human-robot interaction studies, where participant groups must reflect meaningful demographic distributions. Stratified sampling matters because unbalanced data can cause models to underperform on underrepresented cases, and robust, representative sampling is foundational to building AI systems that generalize reliably across real-world conditions.