Every member of a population should have an equal chance of being selected. Random sampling is the technique that achieves that condition. When it is done correctly, the resulting sample is likely to represent the population within a calculable margin of error. Simple random sampling draws names or numbers by chance; more complex designs use stratification or clustering while preserving randomness within stages.

Random sampling underpins the ability to generalize from a sample to a population. Non-random methods—convenience samples, voluntary response samples—may be easier to obtain but introduce bias that cannot be corrected by larger size. Opinion polls, quality-control checks, and many scientific studies rely on random sampling for their validity.

In practice, perfect randomness is often approximated. Incomplete lists, non-response, and practical constraints introduce imperfections. Researchers report response rates and discuss possible biases so that readers can judge the strength of the inference. Despite its difficulties, random sampling remains the gold standard for representative measurement.