Lesson plan
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. Non-sampling errors are much harder to quantify than sampling errors. Non-sampling errors in survey estimates can arise from: * 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. 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),
Article summary
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. Non-sampling errors are much harder to quantify than sampling errors. Non-sampling errors in survey estimates can arise from: * 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. 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),
Key claims
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Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
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Key claims and bibliography are generated from the same tracked claims and sources as the article page.