What Is 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. Sampling is an analysis performed by selecting a number of observations from a larger population, and the selection can produce both sampling errors and non-sampling errors.
[Important: Sampling error can be reduced by randomizing sample selection and/or increasing the number of observations.]
Understanding Sampling Error
Sampling error is the deviation in sampled value versus the true population value due to the fact the sample is not representative of the population or biased in some way. Even randomized samples will have some sampling error since it is only an approximation of the population from which it is drawn.
Sampling error can be eliminated when the sample size is increased and also by ensuring that the sample adequately represents the entire population. Assume, for example, that XYZ Company provides a subscription-based service that allows consumers to pay a monthly fee to stream videos and other programming over the web. The firm wants to survey homeowners who watch at least 10 hours of programming over the web each week and pay for an existing video streaming service. XYZ wants to determine what percentage of the population is interested in a lower-priced subscription service. If XYZ does not think carefully about the sampling process, several types of sampling errors may occur.
- Sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data.
- The results found in the sample thus do not represent the results that would be obtained from the entire population.
- Sampling error can be reduced by randomizing sample selection and/or increasing the number of observations.
Examples of Sampling Error
A population specification error means that XYZ does not understand the specific types of consumers who should be included in the sample. If, for example, XYZ creates a population of people between the ages of 15 and 25 years old, many of those consumers do not make the purchasing decision about a video streaming service because they do not work full-time. On the other hand, if XYZ put together a sample of working adults who make purchase decisions, the consumers in this group may not watch 10 hours of video programming each week.
Selection error also causes distortions in the results of a sample, and a common example is a survey that only relies on a small portion of people who immediately respond. If XYZ makes an effort to follow up with consumers who don’t initially respond, the results of the survey may change. Furthermore, if XYZ excludes consumers who don’t respond right away, the sample results may not reflect the preferences of the entire population.
Factoring in Non-sampling Error
XYZ also wants to avoid non-sampling errors which are caused by human error, such as a mistake made in the survey process. If one group of consumers only watches five hours of video programming a week and is included in the survey, that decision is a non-sampling error. Asking questions that are biased is another type of error.