Reviewed by Oct 05, 2020| Updated on
A sampling error is a mathematical error that arises when an analyst fails to pick a sample representing the entire data population, and the results contained in the sample do not reflect the results that should be obtained from the community as a whole.
Sampling is an analysis done by selecting several observations from a larger population, and the selection may result in both sampling errors and non-sampling errors. Since the sample is not representative of the people or biased in some way, a sampling error is a deviation in sampled values versus actual population value.
Sampling errors can be erased when the sample size increases and also by ensuring that the sample represents the entire population appropriately. For example, assume that XYZ Company provides a subscription-based service that allows consumers to pay a monthly fee for streaming videos and another web-based programming.
The organisation plans to interview homeowners every week who watch at least 10 hours of programming over the internet and pay for an online video streaming service. XYZ would like to determine which percentage of the population is interested in a less expensive subscription service. If XYZ doesn't carefully think about the sampling process, there may be several types of sampling errors.
An error in population specification means that XYZ does not understand the particular types of consumers that should be included in the sample. For example, if XYZ creates a population of people between the ages of 15 and 25, many of those consumers don't make the purchase decision about a video streaming service because they don't work full time.
If XYZ put together a sample of working adults making purchase decisions, this group's consumers may not watch 10 hours of video programming every week.