This document defines probability sampling and describes several probability sampling techniques. It begins by explaining that probability sampling selects subjects with a known probability, giving every unit in the population an equal chance of being selected. It then outlines several specific probability sampling techniques: random sampling, systematic random sampling, stratified random sampling, cluster random sampling, and multi-stage sampling. For each technique, it provides a brief definition and example. The document aims to explain how probability sampling allows researchers to generalize results to the larger population.

1. PROBABILITY
SAMPLING
BY
BHANUTEJA V.R

2. Definition
•A sample that selects subjects with a known
probability.
•Every unit in the population has equal chances of
being selected as a sample unit
•Probability samples are important when one
wishes to generalize to the larger population
because one knows how to weight the responses
to fit the characteristics of the population.

3. Probability Sampling Techniques
• Random Sampling
• Systematic Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Multi-Stage Sampling
• Area Sampling

4. Random Sampling
• This involves selecting anybody from the sample frame
entirely at random.
• Random means that each person within the sample frame
has an equal chance of being selected.
• In order to be random, a full list of everyone within a sample
frame is required.
• Random number tables or a computer is then used to select
respondents at random from the list.

5. Random Sampling

6. Systematic Random Sampling
• This selection is like random sampling but rather than use
random tables or a computer to select your respondents you
select them in a systematic way.
• E.g. every third person on the college list is selected.

7. Systematic Random Sampling

8. Stratified Random Sampling
An appropriate group is decided upon i.e. female, male, 15 –18
year olds and the participants are picked randomly from within
the state

9. Stratified Random Sampling

10. Cluster Random Sampling
• Similar to stratified sampling but the groups are selected for
their geographical location
• i.e. school children within a particular school.
• The school is the cluster with the children being selected
randomly from within the cluster

11. Cluster Random Sampling

12. THANK YOU