Non-sampling error

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]

See also

References
  1. Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. ISBN 0-19-920613-9
  2. Fritz Scheuren (2005). "What is a Margin of Error?", Chapter 10, in "What is a Survey? Archived 2013-03-12 at the Wayback Machine", American Statistical Association, Washington, D.C. Accessed 2008-01-08.
  3. U.S. Census Bureau. March 2012. Introduction. Quarterly Financial Report for Manufacturing, Mining, Trade, and Selected Service Industries. Fourth Quarter 2011. p. xxi
  4. Kalton, Graham. Introduction to survey sampling. Vol. 35. Sage, 1983.
  5. Salant, Priscilla, and Don A. Dillman. "How to Conduct your own Survey: Leading professional give you proven techniques for getting reliable results." (1995).
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