Population And Sample

In statistics the Population (or Universe) means the totality of the items under study — the whole group to which the results of the study are meant to apply. It consists of all the individuals or items that possess the particular characteristic (or set of characteristics) that the survey is about.

The first task in sampling is to identify the population. Once it is identified, the researcher chooses how to study it. If surveying the entire population is not feasible, the researcher instead selects a representative sample — a group or section drawn from the population from which the information will actually be obtained.

A good (representative) sample is generally smaller than the population yet capable of giving reasonably accurate information about it at much lower cost and in much less time. For example, instead of finding every individual's income in a region and averaging it (a costly census needing many enumerators), one can take a representative sample of a few individuals and use their average income as an estimate of the region's average income.

Example. Suppose a researcher's problem is to study the economic condition of agricultural labourers in Churachandpur district of Manipur. Here the population is all agricultural labourers in Churachandpur district, and a practical sample would be ten per cent of the agricultural labourers in Churachandpur district.

Sample surveys are preferred in statistics for several reasons: a sample gives reasonably reliable and accurate information at lower cost and in shorter time; because it is smaller, more detailed and intensive enquiry is possible; and a smaller team of enumerators is easier to train and to supervise effectively.

There are two main types of sampling — random and non-random — explained next. …