Random Sampling
Random sampling is a statistical method used to select a subset of individuals or data points from a larger population, where each member has an equal and independent chance of being chosen. It is fundamental in research, data analysis, and machine learning to ensure unbiased representation and generalizability of results. This technique helps in making inferences about the population without examining every element, saving time and resources.
Developers should learn random sampling when working with large datasets, conducting A/B testing, or building machine learning models to prevent overfitting and ensure fair data splits. It is crucial in scenarios like survey analysis, quality control, and simulation studies where unbiased data selection is needed for accurate predictions and decision-making. For example, in training-validation-test splits for ML, random sampling helps create representative subsets that reflect the overall data distribution.