Which method selects a subset of individuals from a population for analysis?

Random sampling is a statistical technique used to select a subset of individuals from a larger population in such a way that each individual has an equal chance of being chosen. This method helps eliminate bias and ensures that the sample accurately reflects the overall population, which is essential for making valid inferences about the population based on the sample data.

The fundamental principle behind random sampling is to create a representative sample that can be used for various analyses, whether for surveys, experiments, or other studies. This approach is crucial in research because it allows for generalizations about the population while providing a foundation for statistical significance.

One way to understand random sampling is through its practical applications, like in opinion polls or market research, where it's critical to gather opinions from a diverse group without favoring any particular segment. By employing random sampling, researchers can improve the reliability of their conclusions about the entire population.

Alternative methods such as systematic sampling, stratified sampling, and data mining serve different purposes. Systematic sampling involves selecting individuals at regular intervals from a list, which can introduce bias if there's an underlying pattern. Stratified sampling focuses on dividing the population into subgroups and sampling from each, which is effective for ensuring representation of different segments but is not purely random. Data mining, on the