The document explains the concepts of population and sampling in research, distinguishing between target and accessible populations. It discusses the definition of a sample as a representative unit of the target population and outlines principles of sampling, including statistical regularity and optimization. Additionally, it highlights the differences between population and sample in terms of characteristics, data collection methods, and inference-making.

1. POPULATION AND SAMPLING

3. POPULATION
“A population is a complete set of persons or
objects that possess some common
characteristics that is of interest to the
researcher.”
 The population for a study usually is
described as being composed of two groups:
1. Target population
2. Accessible population

4. POPULATION

5. TARGET POPULATION
The target population which is also called
the universe is composed of the entire
group of people or objects to which the
researcher wishes to generalize the
findings of the study.
The target population consists of people or
things that meet the designated set of
criteria of interest to the researcher.

6. ACCESSIBLE POPULATION
It is aggregate of cases that confirm to
designated criteria and are also
accessible as subjects for study.
 By identifying the group from which
the study sample was chosen, the
investigator about the conclusion of the
generalize ability of research findings.

8. SAMPLE
 Sample may be defined as representative unit of target
population which is to be worked upon by researchers during
their study.
 Quantitative researchers often select samples that will
allow them to achieve stating conclusion validity and generalize
their result. They therefore develop a sampling plan that
specifies in advance how study participants are to selected and
how many to include.
 Qualitative researchers make sampling decision during the
course of data collection based on information & theoretical
need & typically do not develop a formal sampling plan in
advance.

10. PRINCIPLES OF SAMPLING
1. Principle or Law of statistical regularity: this
law is based upon mathematical theory of probability.
It is based upon the following two conditions:
 Large sample size: as the sample size increases, the
true characteristics of the population are more likely
to reveal.
 Random selection: the sample should be selected
randomly in which each and every unit of the
universe has an equal chance of being selected.

11. PRINCIPLES OF SAMPLING
2. Principle of inertia of large numbers
– it is based upon the concept that as the
sample size increases the better results we will
get.
For example, if we have to study the weight of
the students studying in a college then fairly
adequate sample of the students help us to
arrive at good results.

12. PRINCIPLES OF SAMPLING
3. Principle of validity: – if valid tests are
derived only then sampling design is termed
as valid.
4. Principle of optimization: – this
principle states that with the help of sample
one must be able to get optimum results
with maximum efficiency and minimum
cost.

13. DIFFERENCE BETWEEN SAMPLE
AND POPULATION
1. Population- refers to the collection of all
elements possessing common characteristics, that
comprises universe.
Sample- means a subgroup of the members of
population chosen for participation in the study.
2. Population- Each and every unit of the group.
Sample- only a handful of units of population.

14. DIFFERENCE BETWEEN SAMPLE
AND POPULATION
3. Population- it focus on identifying the
characteristics.
Sample- it focus on making inferences about
population.
4. Population- data collection is complete
enumeration/census.
Sample- data collection is sample survey or
sampling.