This document defines key concepts in probability and non-probability sampling. It explains that probability sampling uses random selection to select samples from a population, with four main types listed: simple random sampling, stratified sampling, systematic sampling, and cluster sampling. Non-probability sampling relies on the researcher's judgment rather than random selection, with common types being convenience sampling, consecutive sampling, quota sampling, judgmental sampling, and snowball sampling. Examples are provided to illustrate each sampling technique.

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NAME: ABDULAZIZ BELLO
REG NO: 2101271037
COURSE: COMMUNICATION RESEARCH
DEPT: HNDI MASS COMMUNICATION (MO)
TITLE: ASSIGNMENT
QUESTION:
 Define probability
 Types of probability
 Define non probability
 Types of non probability
INTRODUCTION
Probability is the branch of mathematics concerning numerical descriptions of how likely
an event is to occur, or how likely it is that a proposition is true. The probability of an event
is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the
event and 1 indicates certainty.
What is probability sampling?
Probability sampling is a technique in which the researcher chooses samples from a
larger population using a method based on probability theory. For a participant to be
considered as a probability sample, he/she must be selected using a random selection.
Types of probability sampling
There are four commonly used types of probability sampling designs:
 Simple random sampling
 Stratified sampling
 Systematic sampling
 Cluster sampling
Simple random sampling
Simple random sampling gathers a random selection from the entire population, where
each unit has an equal chance of selection. This is the most common way to select a
random sample.
To compile a list of the units in your research population, consider using a random number
generator. There are several free ones available online, such as random.org,
calculator.net, and randomnumbergenerator.org.

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Example: Simple random samplingYou are researching the political views of a
municipality of 4,000 inhabitants. You have access to a list with all 4,000 people,
anonymized for privacy reasons. You have established that you need a sample of 100
people for your research.
Writing down the names of all 4,000 inhabitants by hand to randomly draw 100 of them
would be impractical and time-consuming, as well as questionable for ethical reasons.
Instead, you decide to use a random number generator to draw a simple random sample.
If the first number generated by the program is 1735, this means that resident #1735 on
your list should be selected to be part of the sample. You continue by matching each
number with the respective resident on the list.
Stratified sampling
Stratified sampling collects a random selection of a sample from within certain strata, or
subgroups within the population. Each subgroup is separated from the others on the basis
of a common characteristic, such as gender, race, or religion. This way, you can ensure
that all subgroups of a given population are adequately represented within your sample
population.
For example, if you are dividing a student population by college majors, Engineering,
Linguistics, and Physical Education students are three different strata within that
population.
To split your population into different subgroups, first choose which characteristic you
would like to divide them by. Then you can select your sample from each subgroup. You
can do this in one of two ways:
 By selecting an equal number of units from each subgroup
 By selecting units from each subgroup equal to their proportion in the total
population
Systematic sampling
Systematic sampling draws a random sample from the target population by selecting units
at regular intervals starting from a random point. This method is useful in situations where
records of your target population already exist, such as records of an agency’s clients,
enrollment lists of university students, or a company’s employment records. Any of these
can be used as a sampling frame.

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To start your systematic sample, you first need to divide your sampling frame into a
number of segments, called intervals. You calculate these by dividing your population
size by the desired sample size.
Then, from the first interval, you select one unit using simple random sampling. The
selection of the next units from other intervals depends upon the position of the unit
selected in the first interval.
Cluster sampling
Cluster sampling is the process of dividing the target population into groups, called
clusters. A randomly selected subsection of these groups then forms your sample. Cluster
sampling is an efficient approach when you want to study large, geographically dispersed
populations. It usually involves existing groups that are similar to each other in some way
(e.g., classes in a school).
There are two types of cluster sampling:
 Single (or one-stage) cluster sampling, when you divide the entire population into
clusters
 Multistage cluster sampling, when you divide the cluster further into more clusters,
in order to narrow down the sample size
WHAT IS NON-PROBABILITY SAMPLING?
Definition: Non-probability sampling is defined as a sampling technique in which the
researcher selects samples based on the subjective judgment of the researcher rather
than random selection. It is a less stringent method. This sampling method depends
heavily on the expertise of the researchers. It is carried out by observation, and
researchers use it widely for qualitative research.
Non-probability sampling is a method in which not all population members have an equal
chance of participating in the study, unlike probability sampling. Each member of the
population has a known chance of being selected. Non-probability sampling is most useful
for exploratory studies like a pilot survey (deploying a survey to a smaller sample
compared to pre-determined sample size). Researchers use this method in studies where
it is impossible to draw random probability sampling due to time or cost considerations.

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TYPES OF NON-PROBABILITY SAMPLING
 Convenience sampling:
Convenience sampling is a non-probability sampling technique where samples are
selected from the population only because they are conveniently available to the
researcher. Researchers choose these samples just because they are easy to recruit,
and the researcher did not consider selecting a sample that represents the entire
population.
Ideally, in research, it is good to test a sample that represents the population. But, in some
research, the population is too large to examine and consider the entire population. It is
one of the reasons why researchers rely on convenience sampling, which is the most
common non-probability sampling method, because of its speed, cost-effectiveness, and
ease of availability of the sample.
 Consecutive sampling:
This non-probability sampling method is very similar to convenience sampling, with a
slight variation. Here, the researcher picks a single person or a group of a sample,
conducts research over a period, analyzes the results, and then moves on to another
subject or group if needed. Consecutive sampling technique gives the researcher a
chance to work with many topics and fine-tune his/her research by collecting results that
have vital insights.
 Quota sampling:
Hypothetically consider, a researcher wants to study the career goals of male and female
employees in an organization. There are 500 employees in the organization, also known
as the population. To understand better about a population, the researcher will need only
a sample, not the entire population. Further, the researcher is interested in particular
strata within the population. Here is where quota sampling helps in dividing the population
into strata or groups.
 Judgmental or Purposive sampling:
In the judgmental sampling method, researchers select the samples based purely on the
researcher’s knowledge and credibility. In other words, researchers choose only those
people who they deem fit to participate in the research study. Judgmental or purposive
sampling is not a scientific method of sampling, and the downside to this sampling

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technique is that the preconceived notions of a researcher can influence the results. Thus,
this research technique involves a high amount of ambiguity.
 Snowball sampling:
Snowball sampling helps researchers find a sample when they are difficult to locate.
Researchers use this technique when the sample size is small and not easily available.
This sampling system works like the referral program. Once the researchers find suitable
subjects, he asks them for assistance to seek similar subjects to form a considerably good
size sample.
Non-probability sampling examples
Here are three simple examples of non-probability sampling to understand the subject
better.
1. An example of convenience sampling would be using student volunteers known to
the researcher. Researchers can send the survey to students belonging to a
particular school, college, or university, and act as a sample.
2. In an organization, for studying the career goals of 500 employees, technically, the
sample selected should have proportionate numbers of males and females. Which
means there should be 250 males and 250 females. Since this is unlikely, the
researcher selects the groups or strata using quota sampling.
3. Researchers also use this type of sampling to conduct research involving a
particular illness in patients or a rare disease. Researchers can seek help from
subjects to refer to other subjects suffering from the same ailment to form a
subjective sample to carry out the study.
References
"Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory", Alan Stuart
and Keith Ord, 6th Ed, (2009), ISBN 978-0-534-24312-8.
William Feller, An Introduction to Probability Theory and Its Applications, (Vol 1), 3rd
Ed, (1968), Wiley, ISBN 0-471-25708-7.
Probability Theory The Britannica website
Hacking, Ian (1965). The Logic of Statistical Inference. Cambridge University Press.
ISBN 978-0-521-05165-1.[page needed]
Finetti, Bruno de (1970). "Logical foundations and measurement of subjective
probability". Acta Psychologica. 34: 129–145. doi:10.1016/0001-6918(70)90012-
0.