**Scrutiny of data in statistics : **

Once the data are collected and always they have to be verified for their homogeneity and consistency. This verification of data is called as scrutiny of data.

Since the statistical analyses are made only on the basis of data, it is necessary to check whether the data under consideration are accurate as well as consistence.

No hard and fast rules can be recommended for the scrutiny of data. One must apply his intelligence, patience and experience while scrutinizing the given information.

The data which are collected for the first time by an enumerator or agency are known as primary data

Errors in data may creep in while writing or copying the answer on the part of the enumerator. A keen observer can easily detect that type of error.

Again, there may be two or more series of figures which are in some way or other related to each other. If the data for all the series are provided, they may be checked for internal consistency.

As an example, if the data for population, area and density for some places are given, then we may verify whether they are internally consistent by examining whether the relation

A good statistician can also detect whether the returns submitted by some enumerators are exactly of the same type thereby implying the lack of seriousness on the part of the enumerators.

The bias of the enumerator also may be reflected by the returns submitted by him.

This type of error can be rectified by asking the enumerator(s) to collect the data for the disputed cases once again.

The data are known to be secondary if the data, as being already collected, are used by a different person or agency.

Always we have to be cautious, when we use the secondary data. Usually it is very difficult to verify such data and to edit them to find out inconsistencies, probable errors and omissions.

Scrutinizing the secondary data is vital, because the data may be inaccurate, unsuitable or inadequate.

In the words of Bowley, “It is never safe to take published statistics at their face value without knowing their meanings and limitations and it is always necessary to criticize arguments that can be based on them”.

Data collected by other people cannot be fully depend upon as they may contain many pitfalls and unless they have been thoroughly verified they should not be used.

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