Sampling Methods
Sampling methods are statistical techniques used to select a subset of individuals or data points from a larger population for analysis, enabling efficient and cost-effective research, data collection, and inference. They are fundamental in fields like statistics, data science, market research, and social sciences to draw conclusions about populations without examining every member. Common types include probability sampling (e.g., random, stratified) and non-probability sampling (e.g., convenience, quota).
Developers should learn sampling methods when working with large datasets, conducting A/B testing, performing data analysis, or building machine learning models to handle imbalanced data or reduce computational costs. For example, in data science, sampling is used to create training and test sets, while in web development, it's applied in user behavior analytics or quality assurance testing. It ensures statistical validity and practical feasibility in data-driven projects.