sampling

Sampling is the process of selecting a subset of individuals, observations, or items from a larger population to estimate characteristics of the whole. The aim is to obtain a representative subset that allows conclusions about the population without examining every member. Key terms include population, sample, and sampling frame—the list or mechanism by which members of the population are identified for possible inclusion.

Statistical sampling relies on probability to control sampling error. Probability sampling methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling, each with trade-offs in accuracy and practicality. Non-probability sampling methods—such as convenience, judgment, quota, and snowball sampling—do not give every member a known chance of selection and are more prone to bias but can be useful under constraints.

Important concepts include sample size, margin of error, confidence level, and bias. Representativeness depends on the sampling design and the frame’s completeness. Researchers assess sampling error, account for design effects, and may apply weighting to adjust for unequal selection probabilities or nonresponse.

Procedural steps typically involve defining the target population, choosing a sampling method, determining the sample size, selecting units, collecting data, and analyzing results with appropriate estimation techniques. Proper documentation and reporting are essential for transparency and reproducibility.

Outside statistics, sampling describes techniques in other fields. In signal processing and audio technology, sampling converts a continuous signal into a discrete sequence by recording values at regular intervals, defined by sampling rate and bit depth. In environmental or quality control contexts, sampling helps monitor conditions and assess compliance.