Sampling Error

Meaning and definition of Sampling Error

Sampling error, generally, refers to a statistical error to which an analyst exposes a model only because he/she is working with sample data instead of population or census data. However, using sampling data involves the risk that results found in an analysis might not represent the results that would be acquired by using data involving the whole population from which the sample was derived.

As explained by Investopedia, the use of a sample comparative to a whole population often becomes necessary for various practical and/or financial reasons. Although there are possibilities of some differences to occur between sample analysis results and population analysis results, yet the extent to which these can differ is not projected to be substantial.

The methods of mitigating sampling error include increasing the size of the sample in addition to ensuring that the sample adequately represents the whole population.

Types of sampling errors

• Random sampling errors

In statistics, the sampling error can be found by deducting the value of a parameter from the value of a statistic. This type of sampling error occurs where an estimate of quantity of interest, for example an average or percentage, will generally be subject to sample-to-sample variation. An example of the sampling error in evolution would be a genetic drift – a change in population’s allele frequencies due to chance. The bottleneck effect and the founder effect can be considered as an example of random sampling error.

• Bias problems

Sampling bias is likely to be a source of sampling errors. The bias problems lead to sampling errors which have a prevalence to be either positive or negative. These types of errors are also considered as systematic errors.

• Non-sampling error

Sampling errors can be contrasted to non-sampling errors. The non-sampling error is a catch-all term for the variations from the true value that are not a function of the selected sample, counting different systematic errors as well as any random errors which are not accrued to sampling. Moreover, it is much more difficult to quantify non-sampling errors than the sampling errors.