What are the merits and demerits of Purposive Sampling method as used in Statistics?

This method of sampling is also known as subjective or ‘judgment sampling’ method. Accordingly, investigator himself purposively chooses certain items which to his judgment are best representatives of the universe. Here the selection is deliberate and based on own idea of the investigator about the sample units.

As such under this method, the chance of inclusion of some items in the sample is very high while that of others is very low. However, for better selection of the items under this method certain criteria of selection is first laid down and then the investigator is allowed to make the selection of the items of his own accord within the orbit of those criteria. The relative merits and demerits of this method can be outlined as under:


(i) Economical.


It is less costly and less time consuming.

(ii) Proper Representation:

It ensures proper representation of the universe when the investigation has full knowledge of the composition of the universe and is free from bias,

(iii) Avoid Irrelevant Items:


It prevents unnecessary and irrelevant items entering into the sample per chance.

(iv) Intensive study:

It ensures intensive study of the selected items.

(v) Accurate Results:


It gives better results if the investigator is unbiased and has the capacity of keen observation and sound judgment.


(i) Personal Bias:

There is enough scope for bias or prejudices of the investigate to play and influence the selection.

(ii) No equal chance:


There is no equal chance for all the items of the universe being included in the sample.

(iii) No Degree of Accuracy:

There is no possibility of having any idea about the degree of accuracy achieved in the investigation conducted by this method.

(iv) No Possibility of Sample Error:


There is no possibility of calculating the sample error the idea of which is based on the mathematical concepts which are no applicable to non-random methods of sampling.

(v) Unsuitable for Large Samples:

This method is not suitable for the large samples where the size of both the universe and the sample is considerably large.