Dividing a population into distinct subgroups, or strata, before randomly selecting samples ensures each segment is proportionally represented. This technique reduces sampling error and boosts statistical precision, making it ideal for market research, public health studies, and political polling. Researchers and data scientists benefit by obtaining more accurate, reliable insights from heterogeneous populations without oversampling or bias.
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