Stratified Sampling
Stratified sampling is a statistical sampling technique where a population is divided into homogeneous subgroups called strata based on shared characteristics, and then random samples are drawn from each stratum. This method ensures representation from all subgroups, improving the accuracy and reliability of estimates compared to simple random sampling. It is widely used in surveys, research, and data analysis to reduce sampling error and bias.
Developers should learn stratified sampling when working on data-intensive applications, A/B testing, or machine learning projects where representative data is crucial for model training and validation. It is particularly useful in scenarios with imbalanced datasets, such as fraud detection or medical studies, to ensure minority classes are adequately represented. This technique helps in making more accurate inferences and decisions based on sampled data.