What is a Non-Sampling Error?
A non-sampling error is an error that results during data collection, causing the data to differ from the true values. Non-sampling error differs from sampling error. A sampling error is limited to any differences between sample values and universe values that arise because the entire universe was not sampled.
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 and may be random or systematic. Random errors are believed to offset each other and therefore, most often, are of little concern.
- Sampling and non-sampling errors are terms used in statistics.
- Systematic errors are worse than random errors, which may have little bearing on a study, survey, or census.
- 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.
- 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.
Systematic errors, on the other hand, affect the entire sample and are therefore present a more significant issue. Random errors, generally, will not mean scrapping sample as a whole or 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 will help minimize a sampling error, it will not have any effect on reducing a non-sampling error. Why? Because unfortunately, non-sampling errors are often difficult to detect, and it is virtually impossible to eliminate them.
Non-sampling errors can be described by the following: 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.
Another example: 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 are non-sampling errors described as response errors.
In terms of data collecting from a technical point of view, if errors occur in any data-related entries—such as coding, collection, entry, or editing, they are considered processing errors.