Statistics and probability
Tell me something about the picture
What are the things that you want to study?
What is random sampling?
The leader of the group will present their output in the class.
Rubric for presentation:
Criteria
content
presentation
score
total
100
100
200
Random Sampling
7. The population refers to the whole group under study or
investigation. In research, the population does not always
refer to people. It may mean a group containing elements of
anything you want to study, such as objects, events,
organizations, countries, species, organisms, etc.
8. A sample is a subset taken from a population, either by
random sampling or by non-random sampling. A sample is a
representation of the population where it is hoped that valid
conclusions will be drawn from the population.
Target population
Sample
9. Random sampling is a selection of n elements derived from the
N population, which is the subject of an investigation or
experiment, where each point of the sample has an equal
chance of being selected using the appropriate sampling
technique.
10. Types of Random Sampling Techniques
1. Lottery Sampling
2. Systematic Sampling
3. Stratified Random Sampling
4. Cluster Sampling
5. Multi-stage Sampling
11. Lottery sampling
1. Lottery sampling is a sampling technique in which each
member of the population has an equal chance of being
selected. An instance of this is when members of the
population have their names represented by small pieces of
paper that are then randomly mixed together and picked out.
In the sample, the members selected will be included.
12. Systematic sampling
2. Systematic sampling is a sampling technique in which
members of the population are listed and samples are selected
at intervals called sample intervals. In this technique, every
nth item in the list will be selected from a randomly selected
starting point. For example, if we want to draw a 200 sample
from a population of 6,000, we can select every 3rd person in
the list. In practice, the numbers between 1 and 30 will be
chosen randomly to act as the starting point.
14. Stratified random sampling
3. Stratified random sampling is a sampling procedure in
which members of the population are grouped on the basis of
their homogeneity. This technique is used when there are a
number of distinct subgroups in the population within which
full representation is required. The sample is constructed by
classifying the population into subpopulations or strata on the
basis of certain characteristics of the population, such as age,
gender or socio-economic status. The selection of elements is
then done separately from within each stratum, usually by
random or systematic sampling methods.
15. Stratified random sampling
Example:
Using stratified random sampling, select a sample of 400
students from the population which are grouped according to
the cities they come from. The table shows the number of
students per city.
City Population (N)
A 12,000
B 10,000
C 4,000
D 2,000
Total 28,000
16. Stratified random sampling
Solution:
To determine the number of students to be taken as sample
from each city, we divide the number of students per city by
total population (N= 28,000) multiply the result by the total
sample size (n= 400).
17. Stratified random sampling
City Population (N) Sample (n)
A 12,000 12,000
28,000
× 400 = 171
B 10,000 10,000
28,000
× 400 = 143
C 4,000 4,000
28,000
× 400 = 57
D 2,000 2,000
28,000
× 400 = 29
18. Cluster sampling
4. Cluster sampling is sometimes referred to as area sampling
and applied on a geographical basis. Generally, first sampling
is performed at higher levels before going down to lower levels.
For example, samples are taken randomly from the provinces
first, followed by cities, municipalities or barangays, and then
from households.
19. Multi-stage sampling
5. Multi-stage sampling uses a combination of different
sampling techniques. For example, when selecting
respondents for a national election survey, we can use the
lottery method first for regions and cities. We can then use
stratified sampling to determine the number of respondents
from selected areas and clusters.
20. Do you have any question
class? If none, let us
proceed to activity 1.
21. Direction: Identify the terms being described and
write your answer on a separate sheet of paper.
1. It refers to the entire group that is under study or investigation.
2. It is a subset taken from a population, either by random or non-random
sampling technique. A sample is a representation of the population where
one hopes to draw valid conclusions from about population.
3. This is a selection of n elements derived from a population N, which is the
subject of the investigation or experiment, where each sample point has an
equal chance of being selected using the appropriate sampling technique.
4. A sampling technique where every member of the population has an equal
chance of being selected.
5. It refers to a sampling technique in which members of the population are
listed and samples are selected in intervals called sample intervals.
23. Assignment
Direction: On your answer sheet, give one situation where each of
the sampling methods is being applied.
1. Lottery sampling:
2. Systematic sampling:
3. Stratified random sampling:
4. Cluster sampling:
5. Multi-stage sampling:
