This document discusses different types of sampling procedures used in statistics and population studies. It defines key terms like population and sample. There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling methods like simple random sampling, systematic sampling, stratified sampling, multistage sampling, and cluster sampling ensure each member of the population has an equal chance of being selected. Non-probability methods like convenience sampling, quota sampling, snowball sampling, and clinical trials sampling do not use random selection. Probability sampling allows for better accuracy and generalizability of results to the overall population. The document provides examples and definitions of different sampling methods.

1. Deptt. Community Medicine
SAMPLING
PROCEDURES

2. POPULATION
BIOLOGICAL TERM:
• Total individuals
• at given time
• at given area.
STATISTICS TERM :
Totality of the individuals
observations about which
inferences are to be
made.

3. • World population day :
• world population 2017 :
• Indian population 2016 :
Wednesday , 11 July 2018
7.6 billion as of October 2017
1.324 billion .2016.

4. SAMPLE
A subset of the population

5. WHY SAMPLING?
1. Get information about large populations.
2. Less field time.
3. Less costs.
4. More accuracy i.e. Can Do A Better Job of Data
Collection
5. When it’s impossible to study the whole
population

6. TYPES OF SAMPLING :
PROBABILITY SAMPLING
1. Simple
2. Systemic
3. Stratified
4. Multistage
5. cluster
NON - PROBABILITY SAMPLING
1. Convenience
2. Quota
3. Snow Ball
4. Clinical Trial

7. SYNONYMS
RANDOM SAMPLING :
Probability samples
Non – purposive sampling
NON - RANDOM SAMPLING :
Non-probability samples
purposive sampling

8. CONVENIENCE
(ease of access)
sample is selected from elements of a population that are
easily accessible .

9. SNOWBALL ( friend of friend )

10. QUOTA
chosen out of a specific subgroup.

11. CLINICAL TRIALS SAMPLING

12. Methods used in probability samples
Simple random sampling
Systematic sampling
Stratified sampling
Multi-stage sampling
Cluster sampling

13. RANDOM SAMPLING
each member of the population has an equal probability
of being chosen.
• For 10 samples being chosen out of 250 employs
from a company.

14. Simple random sampling

15. 1. Sampling fraction :
Every kth unit is chosen in the list.
1st unit chosen by random.
SYSTEMATIC RANDOM SAMPLING

16. SYSTEMATIC RANDOM SAMPLING
• 2. Sampling interval ( k):
= Total population
Total sample req.
Eg:
In 1000 people 20 samples are required kth is ?
Eg :
In 48 people 6 samples req. kth is ?

17. Systematic sampling

18. STRATIFIED SAMPLING
• Non homogenous population converted in to
homogenous .
• Eg.: In 1000 population sample req. is 100 :
Total :1000
700
females
300
MALES
70 30

19. Q & A
• For a study every 10th person is selected from a
population , this type of random sampling is :
a) Simple random sampling
b) Stratified random sampling
c) Systematic random sampling
d) Cluster random sampling
All are probability sampling methods except :
a) Quota sampling
b) Stratified sampling
c) Multi-stage sampling
d) Cluster sampling

21. CLUSTER SAMPLING
Cluster :Group of people occurring closely
together.
INDIA use : evaluation of immunization coverage .
WHO use : 30 x 7 technique = 210 children.

22. CLUSTER SAMPLING
Section 4
Section 5
Section 3
Section 2Section 1

23. MULTISTAGE SAMPLING
Done in large county surveys.
PERSON
VILLAGE
DISTRICT
COUNTRY

24. If Randomly: Stratified
If non random : Quota
People are separated in to subgroups and chosen
from each sub-group this sampling is :
a) Systematic sampling
b) Stratified sampling
c) Multi-stage sampling
d) Quota sampling