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distinguish between sampling and non sampling errors..??

Dear Student,
Sampling errors are the errors which are related to the size and nature of the sample which is collected for the study. Due to selecting a small size of sample for the study and due to non-representative nature (which means that sample does not represent the whole population), the value obtained may differ from the actual value of a parameter.It is less serious as it can be corrected by taking relatively larger sample.

Non- Sampling errors are the errors which are related to the collection of data. These can be: (i)Error of measurement (ii) Error of non-response (iii) Error of misinterpretation (iv) Error of calculation (v) Error of sampling bias.
Basically non- sampling error occurs when you do not collect the data properly and effectively. It is a more serious error as compared to sampling errors.

Regards

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Sampling errors occur because inferences about the entire population are based on information obtained from only a sample of that population. Because SLID and the long-form Census are sample surveys, their estimates are subject to this type of error. The coefficient of variationis a measure of the extent to which the estimate could vary, if a different sample had been used. This measure gives an indication of the confidence that can be placed in a particular estimate. This data quality measure will be used later in this paper to help explain why some of SLID's estimates, which are based on a smaller sample, might differ from those of the other programs generating income data.  While the Census is also subject to this type of error, reliable estimates can be made for much smaller populations because the sampling rate is much higher for the Census (20%).

Non-sampling errors can be further divided into coverage errors, measurement errors (respondent, interviewer, questionnaire, collection method…), non-response errors and processing errors. The coverage errors are generally not well measured for income and are usually inferred from exercises of data confrontation.

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