What Is a Non-Sampling Error?
A non-sampling error is a statistical term that refers to an error that results during data collection, causing the data to differ from the true values. A non-sampling error differs from a sampling error. A sampling error is limited to any differences between sample values and universe values that arise because the sample size was limited. (The entire universe cannot be sampled in a survey or a census.)
- A non-sampling error is a term used in statistics that refers to an error that occurs during data collection, causing the data to differ from the true values.
- A non-sampling error refers to either random or systematic errors, and these errors can be challenging to spot in a survey, sample, or census.
- Systematic non-sampling errors are worse than random non-sampling errors because systematic errors may result in the study, survey or census having to be scrapped.
- The higher the number of errors, the less reliable the information.
- When non-sampling errors occur, the rate of bias in a study or survey goes up.
A sampling error can result even when no mistakes of any kind are made. The "errors" result from the mere fact that data in a sample is unlikely to perfectly match data in the universe from which the sample is taken. This "error" can be minimized by increasing the sample size.
Non-sampling errors cover all other discrepancies, including those that arise from a poor sampling technique.
How a Non-Sampling Error Works
Non-sampling errors may be present in both samples and censuses in which an entire population is surveyed. Non-sampling errors fall under two categories: random and systematic.
Random errors are believed to offset each other and therefore, most often, are of little concern. Systematic errors, on the other hand, affect the entire sample and therefore present a more significant issue. Random errors, generally, will not result in scrapping a sample or a census, whereas a systematic error will most likely render the data collected unusable.
Non-sampling errors are caused by external factors rather than an issue within a survey, study, or census.
There are many ways non-sampling errors can occur. For example, non-sampling errors can include but are not limited to, data entry errors, biased survey questions, biased processing/decision making, non-responses, inappropriate analysis conclusions, and false information provided by respondents.
While increasing sample size can help minimize sampling errors, it will not have any effect on reducing non-sampling errors. This is because non-sampling errors are often difficult to detect, and it is virtually impossible to eliminate them.
Non-sampling errors include non-response errors, coverage errors, interview errors, and processing errors. A coverage error would occur, for example, if a person were counted twice in a survey, or their answers were duplicated on the survey. If an interviewer is biased in their sampling, the non-sampling error would be considered an interviewer error.
In addition, it is difficult to prove that respondents in a survey are providing false information—either by mistake or on purpose. Either way, misinformation provided by respondents count as non-sampling errors and they are described as response errors.
Technical errors exist in a different category. If there are any data-related entries—such as coding, collection, entry, or editing—they are considered processing errors.