**Random Sampling**

The
first statistical sampling method is simple random sampling. In this method,
each item in the population has the same probability of being selected as part
of the sample as any other item. For example, a tester could randomly select 5
inputs to a test case from the population of all possible valid inputs within a
range of 1-100 to use during test execution, To do this the tester could use a
random number generator or simply put each number from 1-100 on a slip of paper
in a hat, mixing them up and drawing out 5 numbers. Random sampling can be done
with or without replacement. If it is done without replacement, an item is not
returned to the population after it is selected and thus can only occur once in
the sample.

.

**Systematic Sampling**

Systematic
sampling is another statistical sampling method. In this method, every nth
element from the list is selected as the sample, starting with a sample element
n randomly selected from the first k elements. For example, if the population
has 1000 elements and a sample size of 100 is needed, then k would be 1000/100
= 10. If number 7 is randomly selected from the first ten elements on the list,
the sample would continue down the list selecting the 7th element from each group
of ten elements. Care must be taken when using systematic sampling to ensure
that the original population list has not been ordered in a way that introduces
any non-random factors into the sampling. An example of systematic sampling
would be if the auditor of the acceptance test process selected the 14th
acceptance test case out of the first 20 test cases in a random list of all
acceptance test cases to retest during the audit process. The auditor would
then keep adding twenty and select the 34th test case, 54th test case, 74th
test case and so on to retest until the end of the list is reached.

**Stratified Sampling**

The
statistical sampling method called stratified sampling is used when
representatives from each subgroup within the population need to be represented
in the sample. The first step in stratified sampling is to divide the
population into subgroups (strata) based on mutually exclusive criteria. Random
or systematic samples are then taken from each subgroup. The sampling fraction
for each subgroup may be taken in the same proportion as the subgroup has in
the population. For example, if the person conducting a customer satisfaction
survey selected random customers from each customer type in proportion to the
number of customers of that type in the population. For example, if 40 samples
are to be selected, and 10% of the customers are managers, 60% are users, 25%
are operators and 5% are database administrators then 4 managers, 24 uses, 10
operators and 2 administrators would be randomly selected. Stratified sampling
can also sample an equal number of items from each subgroup. For example, a
development lead randomly selected three modules out of each programming
language used to examine against the coding standard.

**Cluster Sampling**

The
fourth statistical sampling method is called cluster sampling, also called
block sampling. In cluster sampling, the population that is being sampled is
divided into groups called clusters. Instead of these subgroups being
homogeneous based on a selected criteria as in stratified sampling, a cluster
is as heterogeneous as possible to matching the population. A random sample is
then taken from within one or more selected clusters. For example, if an
organization has 30 small projects currently under development, an auditor
looking for compliance to the coding standard might use cluster sampling to
randomly select 4 of those projects as representatives for the audit and then
randomly sample code modules for auditing from just those 4 projects. Cluster
sampling can tell us a lot about that particular cluster, but unless the
clusters are selected randomly and a lot of clusters are sampled,
generalizations cannot always be made about the entire population. For example,
random sampling from all the source code modules written during the previous
week, or all the modules in a particular subsystem, or all modules written in a
particular language may cause biases to enter the sample that would not allow
statistically valid generalization.

**Haphazard Sampling**

There
are also other types of sampling that, while non-statistical (information about
the entire population cannot be extrapolated from the sample), may still
provide useful information. In haphazard sampling, samples are selected based
on convenience but preferably should still be chosen as randomly as possible.
For example, the auditor may ask to see a list of all of the source code
modules, and then closes his eyes and points at the list to select a module to
audit. The auditor could also grab one of the listing binders off the shelf,
flip through it and “randomly” stop on a module to audit. The haphazard
sampling is usually typically, quicker, and uses smaller sample sizes than
other sampling techniques. The main disadvantage of haphazard sampling is that
since it is not statistically based, generalizations about the total population
should be made with extreme caution.

**Judgmental Sampling**

Another
non-statistical sampling method is judgmental sampling. In judgmental sampling,
the person doing the sample uses his/her knowledge or experience to select the
items to be sampled. For example, based on experience, an auditor may know
which types of items are more apt to have nonconformances or which types of
items have had problems in the past or which items are a higher risk to the
organization. In another example, the acceptance tester might select test cases
that exercise the most complex features, mission critical functions or most
used sections of the software.

Catatan:

1. Metode
Sampling ialah suatu metode yang digunakan untuk menyeleksi individual subjek
sedemikian rupa, sehingga subjek tersebut mewakili suatu kelompok besar. Dengan
kata lain yakni metode memilih sample dari suatu populasi.

2.
Jenis
metode sampling: claster-kelompok, stratified-tingkatan, dan random-acak.

Mksh!!!
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