What is Non-Sampling Error?
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A non-sampling error is a statistical term that refers to an error that results during data collection, causing the data to differ from the true values. A non-sampling error differs from a sampling error. A sampling error is limited to any differences between sample values and universe values that arise because the sample size was limited. (The entire universe cannot be sampled in a survey or a census.) A 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.
Definition
Nonsampling error refers to errors that occur during data collection, causing the data to differ from the true values. Unlike sampling error, nonsampling error encompasses all other discrepancies, including those arising from improper sampling techniques.
Origin
The concept of nonsampling error became recognized as statistics evolved. Early statistical analysis focused mainly on sampling error, but as data collection methods became more complex, researchers began to notice the impact of other types of errors on results.
Categories and Features
Nonsampling errors can be categorized into several types, including measurement error, processing error, response error, and coverage error. Measurement error arises from inaccurate measurement tools or methods; processing error occurs during data handling; response error is due to respondents providing inaccurate information; coverage error happens when the sample fails to fully cover the population.
Case Studies
In the 2000 U.S. Census, nonsampling errors led to the underestimation of certain minority populations due to a combination of coverage and response errors. Another example is a market research company conducting a consumer preference survey, where poor questionnaire design led to measurement errors, affecting the accuracy of the survey results.
Common Issues
Investors often overlook the impact of nonsampling errors when using survey data, mistakenly believing the data to be entirely accurate. A common misconception is that all errors can be ignored if the sample is large enough. In reality, nonsampling errors are independent of sample size and need to be minimized through improved data collection and processing methods.
