Simple random sampling is a basic sampling technique where every member of the population has an equal chance of being selected. It aims to produce representative samples free from bias. The document discusses simple random sampling, including its objectives such as when little prior information is known, its advantages like reducing bias, and its disadvantages like potentially not representing the population. It also provides an example of implementing simple random sampling to select a sample of students from a university population.

2. Topic
Simple Random sampling
Presented By:
M.Anwar ul Haq
and
M.Waqas Mahmood

3. Sampling design
The sampling design is a fundamental part of
data collection for scientifically based decision
making.

4. Types of sampling designs
• Judgmental sampling
• Simple random sampling
• Stratified sampling
• Systematic and grid sampling
• Ranked set sampling
• Adaptive cluster sampling
• Composite sampling

5. Simple Random Sampling
Simple random sampling is the most basic and
well-known type of random sampling
technique. In simple random sampling, every
case in the population being sampled has an
equal chance of being chosen. It is an equal
probability sampling method (EPSEM).
EPSEMs are important because they produce
representative samples.

6. Objectives
• To support a decision about whether contamination
levels exceed a threshold of unacceptable risk.
• To monitor trends in environmental conditions or
indicators of health.
• The area/population to sample is relatively
homogeneous (i.e., no major patterns of contamination
or "hot spots" expected) and there is no prior
information or professional knowledge available.
• There is little to no prior information or professional
judgment available.

7. • To protect against any type of selection bias
(for example, when any professional judgment
used to define 'areas' may be challenged)
• It is not possible to do more than the simplest
computations on the resulting data.
• Simple random sampling usually is used in
conjunction with other sampling designs.

8. Advantages
• Freedom from human bias and classification error
remains one of the biggest advantages simple
random sampling offers, as it gives each member
of a population a fair chance of being selected.
• Other sampling methods require much in depth
research and advance knowledge of a population
prior to the selection of subjects. In simple
random sampling, only the complete listing of the
elements in a population (known as the sampling
frame) is needed

9. • A well assembled simple random sample to
have sufficient external validity and sample
being highly representative of a population
• Statistical analysis of the data is relatively
straightforward because most common
statistical analysis procedures assume that the
data were obtained using a simple random
sampling design.
• Explicit formulae, as well as tables and charts
are available for estimating the minimum
sample size needed to support many statistical
analyses

10. Disadvantages
• Randomness of the selection process ensures the
unbiased choice of subjects, it could also, by chance,
lead to the assembly of a sample which does not
represent the population well.
• Data gathering often required a lot of time and labor,
especially in cases involving large target populations.
• Simple random sampling designs ignore all prior
information, or professional knowledge, regarding the
site or process being sampled, Prior information can be
used to develop a probability based sampling design
that is more efficient than simple random sampling.

11. Example
Imagine that a researcher wants to understand
more about the career goals of students at a
single university. Let's say that the university
has roughly 10,000 students. These 10,000
students are our population (N). Each of the
10,000 students is known as unit in order to
select a sample (n) of students from this
population of 10,000 students, we could
choose to use a simple random sample.

12. With simple random sampling, there would an
equal chance (probability) that each of the
10,000 students could be selected for inclusion
in our sample. If our desired sample size was
around 200 students, each of these students
would subsequently be sent a questionnaire to
complete (imagining we choose to collect our
data using a questionnaire).

13. To create a simple random sample,
there are six steps:
(a) Defining the population;
(b) Choosing your sample size;
(c) Listing the population;
(d) Assigning numbers to the units;
(e) Finding random numbers; and
(f) Selecting your sample.

14. (a)Defining the population
In our example, the population is the 10,000
students at the single university. The
population is expressed as N. Since we are
interested in all of these university students,
we can say that our sampling frame is all
10,000 students. If we were only interested in
female university students, for example, we
would exclude all males in creating our
sampling frame, which would be much less
than 10,000 students.

15. (b)Choosing your sample size:
Let's imagine that we choose a sample size of 200
students. The sample is expressed as n. This
number was chosen because it reflects the limit of
our budget and the time we have to distribute our
questionnaire to students. However, we could
have also determined the sample size we needed
using a sample size calculation, which is a
particularly useful statistical tool. This may have
suggested that we needed a larger sample size;
perhaps as many as 400 students.

16. (c) Listing the population:
To select a sample of 200 students, we need to
identify all 10,000 students at the university. If
you were actually carrying out this research,
you would most likely have had to receive
permission from Student Records (or another
department in the university) to view a list of
all students studying at the university.

17. (d) Assigning numbers to the units
We now need to assign a consecutive number
from 1 to N, next to each of the students. In
our case, this would mean assigning a
consecutive number from 1 to 10,000
(i.e., N = 10,000; the population of students at
the university).

18. (e) Finding random numbers
Next, we need a list of random numbers before
we can select the sample of 200 students from
the total list of 10,000 students. These random
numbers can either be found using random
number tables or a computer program that
generates these numbers for you.

19. (f) Selecting your sample:
Finally, we select which of the 10,000 students will
be invited to take part in the research. In this case,
this would mean selecting 200 random numbers from
the random number table. Imagine the first three
numbers from the random number table were:
• 0011(the 11th student from the numbered list of
10,000 students)
• 9292(the 9,292nd student from the list)
• 2001 the 2,001st student from the list)