Sampling (statistics)

Simple random sampling

Simple random sampling

Error creating thumbnail:

Systematic sampling

Stratified sampling

Cluster sampling

Sampling (statistics) is a process used in statistics for selecting a subset of individuals, items, or data from within a statistical population to estimate characteristics of the whole population. Statisticians use various sampling methods to reduce the cost and/or the amount of data that needs to be collected by focusing on only a part of the population. The aim is to obtain a sample that is representative of the population, so that conclusions can be drawn about the population from the sample.

Types of Sampling Methods

There are two broad categories of sampling methods: Probability sampling and Non-probability sampling.

Probability Sampling

In probability sampling, every member of the population has a known, non-zero chance of being selected. Types of probability sampling include:

• Simple Random Sampling: Every member of the population has an equal chance of being selected.
• Stratified Sampling: The population is divided into strata, and random samples are taken from each stratum.
• Cluster Sampling: The population is divided into clusters, and a random sample of these clusters is selected.
• Systematic Sampling: Every nth member of the population is selected.

Non-probability Sampling

In non-probability sampling, not every member of the population has a chance of being selected. Types include:

• Convenience Sampling: Samples are taken from a group that is conveniently accessible.
• Judgment Sampling: The researcher uses their judgment to select members of the population that they think are most representative.
• Quota Sampling: The researcher ensures that certain characteristics are represented in the sample to a certain extent.
• Snowball Sampling: Existing study subjects recruit future subjects from among their acquaintances.

Sampling Error

Sampling error occurs when the sample does not perfectly represent the population. It is the difference between the sample statistic and the actual population parameter. Sampling error can be reduced by increasing the sample size and using a more representative sampling method.

Importance of Sampling

Sampling is crucial in statistics because it allows researchers to obtain information about a population without having to investigate every individual. This makes research more feasible in terms of time, cost, and effort. Sampling is widely used in various fields such as market research, opinion polling, and clinical trials.

Challenges in Sampling

Challenges in sampling include ensuring that the sample is representative of the population, dealing with non-response, and minimizing sampling error. Proper planning, understanding the population, and selecting the appropriate sampling method are essential to address these challenges.

This article is a stub. You can help WikiMD by registering to expand it. Editing is available only to registered and verified users. WikiMD is a comprehensive, free health & wellness encyclopedia.