Population and Sample

A population refers to the full set of units we want to make inferences about, while a sample is the subset selected from that larger group for measurement and analysis. In statistics, we usually cannot access the full population, so we draw conclusions from the sample. For that process to be reliable, the sample must be reasonably representative. In machine learning, the same logic applies when asking how well the training data represents the real-world population. For that reason, the population-sample distinction is fundamental to both classical statistics and data-driven AI systems.