In statistics, non-sampling error is a catch-all term for the deviations of estimates from their true values that are not a function of the sample chosen, including various systematic errors and random errors that are not due to sampling.[1] Non-sampling errors are much harder to quantify than sampling errors.[2]
Non-sampling errors in survey estimates can arise from:[3]
- Coverage errors, such as failure to accurately represent all population units in the sample, or the inability to obtain information about all sample cases;
- Response errors by respondents due for example to definitional differences, misunderstandings, or deliberate misreporting;
- Mistakes in recording the data or coding it to standard classifications;
- Pseudo-opinions given by respondents when they have no opinion, but do not wish to say so
- Other errors of collection, nonresponse, processing, or imputation of values for missing or inconsistent data.[3]
An excellent discussion of issues pertaining to non-sampling error can be found in several sources such as Kalton (1983)[4] and Salant and Dillman (1995),[5]