Difference Between Sampling and Non-Sampling Error

Sampling error is one which occurs due to unrepresentativeness of the sample selected for observation. Conversely, non-sampling error is an error arise from human error, such as error in problem identification, method or procedure used, etc.

An ideal research design seeks to control various types of error, but there are some potential sources which may affect it. In sampling theory, total error can be defined as the variation between the mean value of population parameter and the observed mean value obtained in the research. The total error can be classified into two categories, i.e. sampling error and non-sampling error.

In this article excerpt, you can find the important differences between sampling and non-sampling error in detail.

Content: Sampling Error Vs Non-Sampling Error

1. Comparison Chart
2. Definition
3. Key Differences
4. Conclusion

Comparison Chart

Basis for Comparison	Sampling Error	Non-Sampling Error
Meaning	Sampling error is a type of error, occurs due to the sample selected does not perfectly represents the population of interest.	An error occurs due to sources other than sampling, while conducting survey activities is known as non sampling error.
Cause	Deviation between sample mean and population mean	Deficiency and analysis of data
Type	Random	Random or Non-random
Occurs	Only when sample is selected.	Both in sample and census.
Sample size	Possibility of error reduced with the increase in sample size.	It has nothing to do with the sample size.

Definition of Sampling Error

Sampling Error denotes a statistical error arising out of a certain sample selected being unrepresentative of the population of interest. In simple terms, it is an error which occurs when the sample selected does not contain the true characteristics, qualities or figures of the whole population.

The main reason behind sampling error is that the sampler draws various sampling units from the same population but, the units may have individual variances. Moreover, they can also arise out of defective sample design, faulty demarcation of units, wrong choice of statistic, substitution of sampling unit done by the enumerator for their convenience. Therefore, it is considered as the deviation between true mean value for the original sample and the population.

Definition of Non-Sampling Error

Non-Sampling Error is an umbrella term which comprises of all the errors, other than the sampling error. They arise due to a number of reasons, i.e. error in problem definition, questionnaire design, approach, coverage, information provided by respondents, data preparation, collection, tabulation, and analysis.

There are two types of non-sampling error:

• Response Error: Error arising due to inaccurate answers were given by respondents, or their answer is misinterpreted or recorded wrongly. It consists of researcher error, respondent error and interviewer error which are further classified as under.
  • Researcher Error
    • Surrogate Error
    • Sampling Error
    • Measurement Error
    • Data Analysis Error
    • Population Definition Error
  • Respondent Error
    • Inability Error
    • Unwillingness Error
  • Interviewer Error
    • Questioning Error
    • Recording Erro
    • Respondent Selection Error
    • Cheating Error
• Non-Response Error: Error arising due to some respondents who are a part of the sample do not respond.

Conclusion

To end this discussion, it is true to say that sampling error is one which is completely related to the sampling design and can be avoided, by expanding the sample size. Conversely, non-sampling error is a basket that covers all the errors other than the sampling error and so, it unavoidable by nature as it is not possible to completely remove it.