Sampling error

Sampling Error

A Sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data and the results found in the sample do not represent the results that would be obtained from the entire population.

Definition

In statistics, a sampling error is the gap between a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter. It is the error caused by observing a sample instead of the whole population. The sampling error is due to the fact that the sample is not a perfect representation of the population.

Types of Sampling Error

There are two types of sampling errors: random sampling error and non-random sampling error.

Random Sampling Error

A random sampling error is a statistical fluctuation that occurs because of chance variations in the elements selected for the sample. It is the difference between the statistical result obtained from a sample and the true value existing in the population. This type of error can be reduced by increasing the sample size.

Non-random Sampling Error

A non-random sampling error is a statistical error that occurs when the sample is not representative of the population. This type of error can occur if the researcher selects a sample that is not representative of the population or if the responses are not entirely accurate or truthful.

Causes of Sampling Error

Sampling error can be caused by a variety of factors, including:

• The sample size is too small
• The sample is not representative of the population
• The method of selection is biased
• The data collected is not accurate

Reducing Sampling Error

There are several ways to reduce sampling error, including:

• Increasing the sample size
• Using a random sampling method
• Ensuring the sample is representative of the population
• Collecting accurate data

See Also

• Statistical error
• Non-sampling error
• Margin of error
• Standard error

References

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