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 value of population parameter and the observed value obtained in the research. There are two types of total error, sampling error, and non-sampling error. Many people lack understanding between these two errors, but they are different in the sense that a sampling error is one which occurs due to unrepresentativeness of the sample selected for observation. While a non-sampling error is an error arise from human error. This article makes an attempt to clarify the differences between sampling and non-sampling errors. Have a look.
Content: Sampling Error Vs Non-Sampling Error
|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
- Researcher Error
- Non-Response Error: Error arising due to some respondents who are a part of the sample do not respond.
Key Differences Between Sampling and Non-Sampling Error
The significant differences between sampling and non-sampling error are mentioned in the following points:
- Sampling error is a statistical error happens due to the sample selected does not perfectly represents the population of interest. Non-sampling error occurs due to sources other than sampling while conducting survey activities is known as non-sampling error.
- Sampling error arises because of the variation between the true mean value for the sample and the population. On the other hand, the non-sampling error arises because of deficiency and inappropriate analysis of data.
- Non-sampling error can be random or non-random whereas sampling error occurs in the random sample only.
- Sample error arises only when the sample is taken as a representative of a population.As opposed to non-sampling error which arises both in sampling and complete enumeration.
- Sampling error is mainly associated with the sample size, i.e. as the sample size increases the possibility of error decreases. On the contrary, the non-sampling error is not related to the sample size, so, with the increase in sample size, it won’t be reduced.
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.