Non-sampling error

In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, 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 <a href="/facts/Systematic_error/q7pa7bRt">systematic errors</a> and <a href="/facts/Random_error/q7pa7bRt">random errors</a> that are not due to sampling. Non-sampling errors are much harder to quantify than <a href="/facts/Sampling_error/3k8Fjlb3">sampling errors</a>.
Non-sampling errors in survey estimates can arise from:

<ul><li><a href="/facts/Coverage_error/zh7jyaxw">Coverage errors</a>, such as failure to accurately represent all population units in the sample, or the inability to obtain information about all sample cases;</li>
<li><a href="/facts/Respondent_error/z9lB4rA7">Response errors</a> by respondents due for example to definitional differences, misunderstandings, or deliberate misreporting;</li>
<li>Mistakes in recording the data or coding it to standard classifications;</li>
<li><a href="/facts/Pseudo-opinion/YtxCKYSm">Pseudo-opinions</a> given by respondents when they have no opinion, but do not wish to say so</li>
<li>Other errors of collection, nonresponse, processing, or <a href="/facts/Imputation_(statistics)/UXBwWUKJ">imputation</a> of values for missing or inconsistent data.</li></ul>
An excellent discussion of issues pertaining to non-sampling error can be found in several sources such as Kalton (1983) and Salant and Dillman (1995),

Non-sampling error open-in-new

Non-sampling error