Sampling

Sampling (statistics)

Sampling (statistics) is a method used in statistical analysis where a predetermined number of observations are taken from a larger population. The method of sampling that is used depends on the type of data being studied, but may include simple random sampling, stratified sampling, cluster sampling, and others.

Overview

Sampling is an important aspect of data collection. By studying a sample, statisticians can make inferences about the larger population from which the sample was drawn. The accuracy of these inferences depends on the quality of the sample, which is determined by the sampling method used.

Types of Sampling

There are several different types of sampling methods that can be used, each with its own advantages and disadvantages.

Simple Random Sampling

Simple random sampling is a type of probability sampling method where all members of the population have an equal chance of being selected. This type of sampling is easy to understand and implement, but it may not be the most efficient method if the population is large.

Stratified Sampling

Stratified sampling is a probability sampling method where the population is divided into non-overlapping groups, or strata, and a sample is selected from each stratum. This method is often used when the population is heterogeneous, or when certain strata are of particular interest.

Cluster Sampling

Cluster sampling is a probability sampling method where the population is divided into groups, or clusters, and a sample of clusters is selected. This method is often used when the population is large and spread out over a wide geographic area.

See Also

• Survey methodology
• Data collection
• Statistical inference

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