Non-sampling errors

It is a general assumption in the sampling theory that the true value of each unit in the population can be obtained and tabulated without any errors. In practice, this assumption may be violated due to several reasons and practical constraints. This results in errors in the observations as well as in the tabulation. Such errors which are due to the factors other than sampling are called non-sampling errors.

The non-sampling errors are unavoidable in census and surveys. The data collected by complete enumeration in census is free from sampling error but would not remain free from non-sampling errors. The data collected through sample surveys can have both – sampling errors as well as non-sampling errors. The non-sampling errors arise because of the factors other than the inductive process of inferring about the population from a sample.

In general, the sampling errors decrease as the sample size increases whereas non-sampling error increases as the sample size increases.

In some situations, the non-sampling errors may be large and deserve greater attention than the sampling error.

In any survey, it is assumed that the value of the characteristic to be measured has been defined precisely for every population unit. Such a value exists and is unique. This is called the true value of the characteristic for the population value. In practical applications, data collected on the selected units are called survey values and they differ from the true values. Such difference between the true and observed values is termed as the observational error or response error. Such an error arises mainly from the lack of precision in measurement techniques and variability in the performance of the investigators.

Sources of non-sampling errors:

Non sampling errors can occur at every stage of planning and execution of survey or census. It occurs at planning stage, field work stage as well as at tabulation and computation stage. The main sources of the non sampling errors are :-
1) lack of proper specification of the domain of study and scope of investigation,
2)incomplete coverage of the population or sample,
3)faulty definition,
4)defective methods of data collection and
5)tabulation errors.

Sampling error:

If complete accuracy can be ensured in the procedures such as determination, identification and observation of sample units and the tabulation of collected data, then the total error would consist only of the error due to sampling, termed as sampling error.
Measure of sampling error is mean squared error (MSE). The MSE is the difference between the estimator and the true value and has two components: –

1)square of sampling bias. –
2)sampling variance.

If the results are also subjected to the non-sampling errors, then the total error would have both sampling and non-sampling error.