Principles of Sampling

Definition: The Sampling is a statistical analysis tool wherein the data are collected from a few representative items of the universe, called as a sample, on the basis of which the characteristic of the entire population can be ascertained.

Principles of Sampling

There are two important principles of sampling on which the sampling theory depends on:

1. Principle of ‘Statastical Regularity’: The principle of statistical regularity is derived from the theory of probability in mathematics. According to this principle, when a large number of items is selected at random from the universe, then it is likely to possess the same characteristics as that of the entire population.

   This principle asserts that the sample selection is random, i.e. every item has an equal and likely chance of being selected. It is believed that sample selected randomly and not deliberately acts as a true representative of the population. Thus, this principle is characterized by the large sample size and the random selection of a representative sample.

2. Principle of ‘Inertia of Large Numbers’: The principle of Inertia of large numbers states that the larger the size of the sample the more accurate the conclusion is likely to be. This principle is based on the notion, that large numbers are more stable in their characteristics than the small numbers, and the variation in the aggregate of large numbers is insignificant. It does not mean that there is no variation in the large numbers, there is, but is less than in the smaller numbers.

Thus, both the principles talk about the sample size i.e. the larger it is, the larger is the accuracy of the results obtained.