# Sampling error

Sampling error

## Description

### Random sampling

In statistics, sampling error or estimation error is the error caused by observing a sample instead of the whole population. [1] The sampling error can be found by subtracting the value of a parameter from the value of a statistic.[citation needed] In nursing research, a sampling error is the difference between a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter (Bunns & Grove, 2009). An estimate of a quantity of interest, such as an average or percentage, will generally be subject to sample-to-sample variation.[1] These variations in the possible sample values of a statistic can theoretically be expressed as sampling errors, although in practice the exact sampling error is typically unknown. Sampling error also refers more broadly to this phenomenon of random sampling variation.

An example of a sampling error in evolution is genetic drift; a change is a population’s allele frequencies due to chance. For example the bottleneck effect; when natural disasters dramatically reduce the size of a population resulting in a small population that may or may not fairly represent the original population. What makes the bottleneck effect a sampling error is that certain alleles, due to natural disaster, are more common while others may disappear completely, making it a sampling error. Another example of genetic drift that is a sampling error is the founder effect. The founder effect is when a few individuals from a larger population settle a new isolated area. In this instance, there are only a few individuals with little gene variety, making it a sampling error. [2]

The likely size of the sampling error can generally be controlled by taking a large enough random sample from the population,[3] although the cost of doing this may be prohibitive; see sample size and statistical power for more detail. If the observations are collected from a random sample, statistical theory provides probabilistic estimates of the likely size of the sampling error for a particular statistic or estimator. These are often expressed in terms of its standard error.

### Bias problems

Sampling bias is a possible source of sampling errors. It leads to sampling errors which either have a prevalence to be positive or negative. Such errors can be considered to be systematic errors.

### Non-sampling error

Sampling error can be contrasted with non-sampling error. Non-sampling error is a catch-all term for the deviations from the true value that are not a function of the sample chosen, including various systematic errors and any random errors that are not due to sampling. Non-sampling errors are much harder to quantify than sampling error.[3]