SAMPLING AND SAMPLING DISTRIBUTION
• Population
A group of all possible elements or objects are called population, for example, Human
Population, the total number of students in college. The number of elements involved in
population is called size of the population. It is denoted by N.
• Finite Population
A population said to be finite if it consists of a finite or fixed number of elements for example,
All university students In Pakistan, the weights of all students enrolled at Punjab University.
• Infinite Population
A population said to be infinite if there is not limit to the number of elements. For example, All
heights between 2 and 3 meters.
• Existent Population
A population which consists of concrete objects is called an existent population.
• Hypothetical Population
A population which does not contain concrete objects or items is called hypothetical population.
• Sample
Representation small part of a population is called sample. The number of elements desired in
sample is called sample size. It is denoted by n.
• Sampling
Technique of selecting a true sample is called sampling. Sampling is broadly (mostly)
distributed into two classes.
a) Probability or Random Sampling
b) Non-probability or Non-random Sampling
a) Probability or Random Sampling
Technique of sampling where every sampling unit is selected untirely at random, therefore
every sampling unit have same chances of selection in the sample, the probability involved
in the selection of sampling unit such a technique is called probability sampling.
Some Important probability samplings are
1. Simple random sampling
2. Stratified sampling
3. Systematic sampling
4. Cluster sampling
5. Multistage and Multiphase sampling
b) Non-probability or Non-random Sampling
In non probability sampling, the selection of the elements is not base on probability theory
but the personal judgment plays a significant role in the selection of the sample the
examples of non probability sampling are.
1. Judgment or Purposive Sampling
2. Q