This document discusses different probability sampling techniques, including simple random sampling, systematic random sampling, and stratified random sampling. Simple random sampling involves randomly selecting subjects from a population where each member has an equal chance of selection. Systematic random sampling involves randomly selecting the first subject and then selecting every nth subject thereafter. Stratified random sampling involves dividing the population into subgroups or strata and then randomly selecting subjects proportionally from each strata. The document provides examples and discusses the advantages and disadvantages of each technique.

1. Anam Nasir (054)
MBA 5th Semester
Probability Sampling and Its
Techniques

2. Probability Sampling
Types
Unrestricted Restricted
Simple random Sampling Complex Probability Sampling
1-Systematic Random Sampling
2-Stratified Random Sampling
Proportionate
Dis- Proportionate

3. Techniques: The process of selecting a sample that allows individual in
the defined population to have an equal and independent chance of being
selected for the sample.

4. Simple Random Sampling
 Applicable when population is small, homogeneous & readily
available •
 Simple random sampling is the basic sampling technique
where we select a group of subjects (a sample) for study from
a larger group (a population). Each individual is chosen
entirely by chance and each member of the population has an
equal chance of being included in the sample. Every possible
sample of a given size has the same chance of selection.

5. Example: Simple Random Sampling

6. Continued..,
ADVANTAGES OF SIMPLE RANDOM SAMPLING:
 Easy to conduct.
 Strategy requires minimum knowledge of the population
 No need of prior information of population.
DISADVATAGES OF SIMPLE RANDOM SAMPLING:
 Need names of all population members.
 Larger sample needed.
 Produces larger errors.
 High cost.

7. Systematic Random Sampling
• Systematic sampling is a random sampling technique which is frequently chosen
by researchers for its simplicity and its periodic quality.
• The procedure involved in systematic random sampling is very easy and can be
done manually.
 The researcher first randomly picks the first item or subject from the population.
 Then, the researcher will select each n'th subject from the list.
Procedure:
• Starting number:
The researcher selects an integer that must be less than the total number of
individuals in the population. This integer will correspond to the first subject.
• Interval:
The researcher picks another integer which will serve as the constant difference
between any two consecutive numbers in the progression. The integer is typically
selected so that the researcher obtains the correct sample size

8. Example: Systematic Random
Sampling

9. Continued..,
• Advantages:
• Sample easy to select.
• Suitable sampling frame can be identified easily.
• Easier and less costlier method.
• High precision.
• Disadvantages:
• Need complete list of units.
• Moderate cost
• Each element does not get equal chance.

10. Stratified Random Sampling
• DEFINITION OF STRATIFIED SAMPLING:
• A stratified sample is a probability sampling technique in
which the researcher divides the entire target population into
different;
 Subgroups, or Strata.
• And then randomly selects the final subjects proportionally
from the different strata. This type of sampling is used when
the researcher wants to highlight specific subgroups within
the population.

11. Example: Stratified Random Sampling

12. When to use Stratified Sampling?
• There are many situations in which researchers would choose stratified
random sampling over other types of sampling.
• First, it is used when the researcher wants to highlight a specific subgroup
within the population. Stratified sampling is good for this because it
ensures the presence of key subgroups within the sample.
• Researchers also use stratified random sampling when they want to
observe relationships between two or more subgroups.
• With this type of sampling, the researcher is guaranteed subjects from
each subgroup are included in the final sample, whereas simple random
sampling does not ensure that subgroups are represented equally or
proportionately within the sample.

13. Strata & Subgroups

14. Example

15. Types of Stratified Random Sampling
• Proportionate Stratified
Random Sample:
In proportional stratified
random sampling, the
size of each strata is
proportionate to the
population size of the
strata when looked at
across the entire
population.
• Disproportionate
Stratified Random
Sample:
In disproportionate
stratified random
sampling, the different
strata do not have the
same sampling fractions
as each other.

16. Continued..,
• ADVANTAGES OF STRATIFIED RANDOM SAMPLING:
• More precise sample.
• Can be used both proportions and stratification sampling.
• Sample represents the desired strata.
• DISADVANTAGES OF STRATIFIED RANDOM SAMPLING:
• Need names of all population members.
• There is difficulty in reaching all selected in the sample.
• Researcher must have names of all populations.