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Fig 2.1.1 Sampling techniques

2.1.4.1 Random Sampling
This technique implies that every unit in the population has an
equal and non-zero chance of being selected. There is no bias on
the part of the researcher and the sample selected is at random. For
◆ Equal chance of example, if a teacher tries to select five students for a task from the
selection list of students in the class without any prejudice, every student has
an equal chance of being selected. This would be a representative
sample which can be generalised to the population. By reducing
sampling bias, more closeness to the population can be ensured.
Random sampling is also known as Probability Sampling.

Types of random sampling
A. Simple random sampling

This is the easiest way of random sampling, where samples
are chosen effortlessly. Each item has an equal chance of being
included in the sample. For example, in a class of 50 students,
we can easily select 10 students using simple random sampling.
◆ Equal and When dealing with an infinitely large population, each item in
independent chance the random sample is chosen with the same probability, and each
selection is independent of the others. Simple random sampling
consists of Lottery method and random number table method.
a. Lottery method

The lottery method is a simple random sampling technique used
to select a sample from a population. In the previous example,

56 SGOU - SLM -MCom Research Methodology