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# Sampling and Non-Sampling Errors

The errors involved in the collection, processing and analysis of the data may be broadly classified into two categories namely,

Sampling and Non-Sampling Errors:

A sample is a part of the whole population. A sample drawn from the population depends upon chance and as such all the characteristics of the population may not be present in the sample drawn from the same population. The errors involved in the collection, processing and analysis of the data may be broadly classified into two categories namely,

(i) Sampling Errors

(ii) Non-Sampling Errors

(i) Sampling Errors

Errors, which arise in the normal course of investigation or enumeration on account of chance, are called sampling errors. Sampling errors are inherent in the method of sampling. They may arise accidentally without any bias or prejudice. Sampling Errors arise primarily due to the following reasons:

(a) Faulty selection of the sample instead of correct sample by defective sampling technique.

(b) The investigator substitutes a convenient sample if the original sample is not available while investigation.

(c) In area surveys, while dealing with border lines it depends upon the investigator whether to include them in the sample or not. This is known as Faulty demarcation of sampling units.

(ii) Non-Sampling Errors

The errors that arise due to human factors which always vary from one investigator to another in selecting, estimating or using measuring instruments( tape, scale)are called Non-Sampling errors.It may arise in the following ways:

(a) Due to negligence and carelessness of the part of either investigator or respondents.

(b) Due to lack of trained and qualified investigators.

(c) Due to framing of a wrong questionnaire.

(d) Due to apply wrong statistical measure

(e) Due to incomplete investigation and sample survey.

Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail
12th Business Maths and Statistics : Chapter 8 : Sampling Techniques and Statistical Inference : Sampling and Non-Sampling Errors |