This document provides information about sampling, including definitions, reasons for using sampling, what constitutes a good sample, and different types of sampling methods. It defines sampling as selecting a portion of a larger group to make inferences about the whole group in a way that is accurate, economical, reliable, and fast. A good sample is both valid and precise. It then outlines the key steps in sampling design: identifying the target population and parameters of interest, developing a sampling frame, choosing an appropriate sampling method (probability or non-probability), and determining sample size. The document proceeds to classify and describe different probability sampling methods, including simple random sampling, systematic sampling, stratified random sampling, and cluster sampling.

1. Sample
- Kamrun Nahar Etu

2. Definition
Measuring a small portion of something
and then making a general statement
about the whole thing.
Process of selecting a number of units for a study in
such a way that the units represent the larger group
from which they are selected.

3. Why we need Sampling
Very accurate
Economical in nature
Very reliable.
 High suitability ratio
towards the different
surveys.
Takes less time

4. WHAT IS A GOOD SAMPLE?
• The sample must be valid.
• Validity depends on 2 considerations:
1. Accuracy
2. Precision

5. 1. What is the
target
population?
- Target
population is
the aggregation
of elements
(members of
the population)
from which the
sample is
actually
selected.
STEPS IN SAMPLING DESIGN
2. What are the
parameters of
interest?
- Parameters
are summary
description of
a given
variable in a
population.
3. What is the
sampling
frame?
- Sampling
frame is the
list of
elements
from which
the sample is
actually
drawn.
Complete and
correct list of
population
members
only.

6. 4. What is
the
appropriat
e sampling
method?
-
Probability
or Non-
Probability
sampling
method
5. What size
sample is
needed?
- There are no
fixed rules in
determining the
size of a sample
needed. There
are guidelines
that should be
observed in
determining the
size of a
sample.

7. Classification of Sample

8. Probability Sampling
- Based on the concept of random
selection
- A controlled procedure
- Assures that each population
element is given a known nonzero
chance of selection.

9. Non-probability Sampling
-In contrast, is arbitrary (non random)
and subjective
-
Allowing interviewers to choose
sample elements “at random”

10. Probability Sampling

11. Simple Random
Sampling
 Each element in the
target population has
an equal chance or
probability of being
selected in the
population
 Numbers can be
randomly generated by
computers or picked
out of a box
 In small population
random sampling is
done without
replacement

12. Systematic
sampling
 only the first unit is
selected randomly and
the remaining units of
the sample are selected
at fixed intervals
 For instance, if a 4
per cent sample is
desired, the first item
would be selected
randomly from the first
twenty-five and
thereafter every 25th
item would
automatically be
included in the sample.

13.  Process of grouping
members into relatively
homogenous groups
before sampling
 Each element of the
population must be
included in a stratum
Strata should be
exhaustive so as not to
leave any element of
the population
Then random
sampling is applied
within each stratum
Stratified
Random
Sampling

14. Cluster sampling
 Thus in cluster
sampling the total
population is divided
into a number of
relatively small
subdivisions which are
themselves clusters of
still smaller units and
then some of these
clusters are randomly
selected for inclusion in
the overall sample.