Some of the commonly known and frequently used types of sampling are:

- Purposive sampling
- Random sampling
- Stratified sampling
- Systematic sampling

Below we will precisely these terms, without entering into detailed discussion.

Purposive sampling is one in which the sample units are selected with definite purpose in view. For example, if we want to give the picture that the standard of living has increased in the city of New Delhi, we may take individuals in the sample from rich and posh localities like Defence Colony, South Extension, Golf Links, Jor Bagh, Chanakyapuri, Greater Kailash etc. and ignore the localities where low income group and the middle class families live. This sampling suffers from the drawback of favouritism and nepotism and does not give a representative sample of the population.

In this case the sample units are selected at random and the drawback of purposive sampling, viz., favouritism or subjective element, is completely overcome. A random sample is one in which each unit of population has an equal chance of being included in it.

Suppose we take a sample of size n from a finite population of size N. then there are NCn possible samples. A sampling technique in which each of the NCn samples has an equal chance of being selected is known as random sampling and the sample obtained by this technique is termed as a random sample.

Proper care has to be taken to ensure that the selected sample is random. Human bias, which varies from individual to individual, is inherent in any sampling scheme administered by human beings. Fairly good random samples can be obtained by the use of Tippet’s random number tables or by throwing or a dice, draw of a lottery, etc.

The simplest method, which is normally used, is the lottery system which is illustrated below by means of an example.

Suppose we want to select ‘r’ candidates out of n. we assign the numbers one to n, one number to each candidate and write these numbers on n slips which are made as homogeneous as possible in shape, size, etc. these slips are then put in a bag and thoroughly shuffled and then ‘r’ slips are drawn one by one. The ‘r’ candidates corresponding to the numbers on the slips drawn will constitute the random sample.

Simple sampling is random sampling in which each unit of the population has an equal chance, say p, of being included in the sample and that this probability is independent of the previous drawings. Thus a simple sample of size n from a population may be identified with a series of n independent trials with constant probability ‘p’ of success for each trial.

Here the entire heterogeneous population is divided into a number of homogeneous groups, usually termed as strata, which differ from one another but each of these groups is homogenous within itself. Then units are sampled at random from each of this stratum, the stratum in the population. The sample, which is the aggregate of the sampled units of each of the stratum, is termed as stratified sample and the technique of drawing this sample is known as stratified sampling. Such a sample is by far the best and can safely be considered as representative of the population from which it has been drawn.

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- Procedure for testing of hypothesis
- Test Of Significance For Large Samples
- Sampling Of Attributes
- Test of significance for difference of proportions
- Sampling of Variables
- Standard Error Of Sample Mean
- Test Of Significance For Single Mean
- Test Of Significance For Difference Of Means
- Test Of Significance For The Difference Of Standard Deviations

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