Sampling error

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Sampling Error

Sampling error (pronunciation: /ˈsamplɪŋ ˈɛrər/) is a statistical term that refers to the discrepancy between a sample statistic and the true population parameter. This error occurs when a sample subset of the population is used to represent the entire population.

Etymology

The term "sampling error" is derived from the English words "sample" and "error". "Sample" (from the Old French esample) refers to a small part or quantity intended to show what the whole is like. "Error" (from the Latin error), refers to a mistake.

Types of Sampling Error

There are two main types of sampling error: systematic sampling error and random sampling error.

  • Systematic sampling error occurs when the method used to select the sample causes the sample to differ from the population in some systematic way. For example, if a survey is conducted by telephone, those without telephones are automatically excluded, which could lead to a systematic bias in the results.
  • Random sampling error occurs due to the random nature of sampling. Even if the sampling process is completely random and unbiased, there will still be some degree of sampling error. This is because a sample, by definition, contains less information than the population from which it is drawn.

Related Terms

  • Bias: A systematic error in an estimate or in descriptive statistics.
  • Population: The entire group that you want to draw conclusions about.
  • Sample: A smaller group selected from a population.
  • Statistic: A single measure of some attribute of a sample.

See Also

References

  • Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth Cengage.
  • Fowler, F. J. (2009). Survey research methods (4th ed.). Thousand Oaks, CA: Sage Publications.

External links

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