Contains brief description of hypothesis and sampling

1. HYPOTHESIS
&
SAMPLING
Dr. S. BELLARMIN DIANA
ASSISTANT PROFESSOR
DEPARTMENT OF MANAGEMENT STUDIES
BON SECOURS COLLEGE FOR WOMEN, THANJAVUR

2. HYPOTHESIS
Hypothesis is the composition of some variables which have some specific position or role of the
variables i.e., to be verified empirically. It is a proposition about the factual and conceptual
elements. Hypothesis is called a leap into the dark. It is a brilliant guess about the solution of a
problem.
Hypothesis is the composition of some variables which have some specific position or role of the
variables i.e. to be verified empirically. It is a proposition about the factual and conceptual
elements. Hypothesis is called a leap into the dark. It is a brilliant guess about the solution of a
problem.
Hypothesis is the composition of some variables which have some specific position or role of the
variables i.e. to be verified empirically. It is a proposition about the factual and conceptual
elements. Hypothesis is called a leap into the dark. It is a brilliant guess about the solution of a
problem.
ASSUMPTION, POSTULATE AND HYPOTHESIS
Assumption: Assumption means restrictive conditions before the argument can become valid.
Assumptions are made on the basis of logical insight and their truthfulness can be observed on
the basis of data or evidences.
Postulate: Postulates are the working beliefs of most scientific activity. A postulate is a
statement assumed to be true without need of proof of any kind.
Hypothesis: A hypothesis is different from both of these. It is the presumptive statement of a
proposition which the investigator seeks to prove. It is a condensed generalization. This
generalization requires knowledge of principles of things or essential characteristics which
pertain to entire class of phenomena.

3. NATURE OF HYPOTHESIS
The hypothesis is a clear statement of what is intended to be investigated. It should be specified
before research is conducted and openly stated in reporting the results. This allows to –
Identify…
▪ the research objectives;
▪ the key abstract concepts involved in the research; and
▪ its relationship to both the problem statement and the literature review.
The following are the main features of a hypothesis –
It…
Is conceptual in nature.
Is a verbal statement in a declarative form.
Has the empirical referent.
Indicates the tentative relationship between two or more variables.
Is a powerful tool of advancement of knowledge, consistent with existing knowledge
and conducive to further enquiry
Can be tested, verifiable or falsifiable.
Is not moral or ethical questions.
Is neither too specific nor to general.
Is a prediction of consequences
Is considered valuable even if proven false.
FUNCTIONS / ROLES OF HYPOTHESIS
A hypothesis serves as a sound guide to:

4. (i) the kind of data that must be collected in order to answer the research problem;
(ii) (ii) the way in which the data should be organized most efficiently and meaningfully,
and
(iii) (iii) the type of methods that can be used for making analysis of the data.
A hypothesis also performs the following significant functions –
Test theories: A hypothesis, when empirically proved, helps us in testing an existing theory. A
theory is not a mere speculation, but it is built upon facts. It is a set of inter-related propositions
or statements organized into a deductive system that offers an explanation of some phenomenon.
Facts constitute a theory when they are assembled, ordered and seen in a relationship. Therefore,
when a hypothesis is ‘tested’, it not only supports the existing theory that accounts for description
of some social phenomenon but also in a way ‘tests’ it.
Suggest new theories: A hypothesis, even though related to some existing theory, may, after
tested, reveal certain ‘facts’ that are not related to the existing theory or disclose relationships
other than those stated in the theory. It does not support the existing theory but suggests a new
theory.
Describe social phenomenon: A hypothesis also performs a descriptive function. Each time a
hypothesis is tested empirically, it tells us something about the phenomenon it is associated with.
If the hypothesis is empirically supported, then our information about the phenomenon increases.
Even if the hypothesis is refuted, the test tells us something about the phenomenon we did not
know before.
Suggest social policy: A hypothesis, after its testing, may highlight such ‘ills’ of the existing
social or legislative policy. In such a situation, the tested hypothesis helps us in formulating (or
reformulating) a social policy. It may also suggest or hint at probable solutions to the existing
social problem(s) and their implementation.
The hypotheses play significant role in the scientific studies. The following are some of the
important role and functions of the hypothesis -

5. Helps in the testing of the theories.
Serves as a great platform in the investigation activities.
Provides guidance to the research work or study.
Hypothesis sometimes suggests theories.
Helps in knowing the needs of the data.
Explains social phenomena.
Develops the theory.
Also acts as a bridge between the theory and the investigation.
Provides a relationship between phenomena in such a way that it leads to the empirical
testing of the relationship.
Helps in knowing the most suitable technique of analysis.
Helps in the determination of the most suitable type of research.
Provides knowledge about the required sources of data.
Research becomes focused under the direction of the hypothesis.
It is very helpful in carrying out an enquiry of a certain activity.
Helps in reaching conclusions, if it is correctly drawn.
IMPORTANCE OF HYPOTHESIS
Hypothesis as the Investigator’s ‘Eyes’: By guiding the investigator in further investigation it
serves as the investigator’s ‘Eyes’ in seeking answers to tentatively adopted generalization.
It Focuses Research: Without it, research is unfocussed research and remains like a random
empirical wandering. It serves as necessary link between theory and the investigation.
It Places Clear and Specific Goals: A well thought out set of hypothesis is that they place clear
and specific goals before the research worker and provide researcher with a basis for selecting
sample and research procedure to meet these goals.
It Links Together: It serves the important function of linking together related facts and
information and organizing them into wholes.

6. It Prevents Blind Research: The use of hypothesis prevents a blind search and indiscriminate
gathering of masses of data which may later prove irrelevant to the problem under study.
As a Sort of Guiding Light: A hypothesis serves as a powerful beacon that lights the way for
the research work.
Van Dalen advocates the importance of hypothesis in the following ways -
Hypotheses are indispensable research instrument, for they build a bridge between the
problem and the location of empirical evidence that may solve the problem.
A hypothesis provides the map that guides and expedites the exploration of the phenomena
under consideration.
A hypothesis pin points the problem. The investigator can examine thoroughly the factual
and conceptual elements that appear to be related to a problem.
Using hypothesis determines the relevancy of facts. A hypothesis directs the researcher’s
efforts into a productive channels.
The hypothesis indicates not only what to look for is an investigation but how to obtain
data. It helps in deciding research design. It may suggest what subjects, tests, tools, and
techniques are needed.
The hypothesis provides the investigator with the most efficient instrument for exploring
and explaining the unknown facts.
A hypothesis provides the framework for drawing conclusions.
These hypotheses simulate the investigator for further research studies.
CHARACTERISTICS OF A GOOD HYPOTHESIS
A ‘workable’ or ‘usable’ hypothesis would be the one that satisfies many of the following
criteria.
Hypothesis should be conceptually clear: The concepts used in the hypothesis should be
clearly defined, not only formally but also, if possibly, operationally. Formal definition of the
concepts will clarify what a particular concept stands for, while the operational definition will

7. leave no ambiguity about what would constitute the empirical evidence or indicator of the
concept on the plane of reality. Obviously, an undefined or ill-defined concept makes it
difficult or rather impossible for the researcher to test hypothesis as there will not be any
standard basis for researcher to know the observable facts. However, a researcher, while
defining concepts, should use, as far as possible, the terms that are communicable or
definitions that are commonly accepted. It should be stated as far as possible in most simple
terms so that it can be easily understandable all concerned. Researcher should not create ‘a
private world of words’.
Hypothesis should be specific: No vague or value-judgmental terms should be used in
formulation of a hypothesis. It should specifically state the posited relationship between the
variables. It should include a clear statement of all the predictions and operations indicated
therein and they should be precisely spelled out. Specific formulation of a hypothesis assures
that research is practicable and significant. It helps to increase the validity of results because
the more specific the statement or prediction, the smaller the probability that it will actually
be borne out as a result of mere accident or chance. A researcher, therefore, must remember
that narrower hypothesis is generally more testable and s/he should develop such a hypothesis.
Hypothesis should be empirically testable: It should have empirical referents so that it will
be possible to deduce certain logical deductions and inferences about it. Therefore, a
researcher should take utmost care that his/her hypothesis embodies concepts or variables that
have clear empirical correspondence and not concepts or variables that are loaded with moral
judgments or values. Such statements as ‘criminals are no worse than businessmen’,
‘capitalists exploit their workers’, ‘bad parents beget bad children’, ‘bad homes breed
criminality’, or ‘pigs are well named because they are so dirty’ can hardly be usable
hypotheses as they do not have any empirical referents for testing their validity. In other
words, a researcher should avoid using terms loaded with values or beliefs or words having
moral or attitudinal connotations in his hypothesis.
Hypothesis should be related to available techniques: Researcher may ignorance of the
available techniques, makes him/her weak in formulating a workable hypothesis. A

8. hypothesis, therefore, needs to be formulated only after due thought has been given to the
methods and techniques that can be used for measuring the concepts or variables incorporated
in the hypothesis.
Hypothesis should be related to a body of theory or some theoretical orientation: A
hypothesis, if tested, helps to qualify, support, correct or refute an existing theory, only if it
is related to some theory or has some theoretical orientation. A hypothesis imaginatively
formulated does not only elaborate and improve existing theory but may also suggest
important links between it and some other theories. Thus, exercise of deriving hypothesis
from a body of theory may also be an occasion for scientific leap into newer areas of
knowledge.
A hypothesis derived from a theory invests its creator with the power of prediction of its
future. The potency of hypothesis in regard to predictive purpose constitutes a great
advancement in scientific knowledge. A genuine contribution to knowledge is more likely to
result from such a hypothesis. A hypothesis, it is said, to be preferred is one which can predict
what will happen, and from which we can infer what has already happened, even if we did
not know (it had happened) when the hypothesis was formulated.
SOURCES OF HYPOTHESIS
A hypothesis or a set of hypotheses may originate from a variety of sources. The source of
hypothesis, however, has an important bearing on the nature of contribution in the existing
body of knowledge. A few prominent sources of hypothesis are discussed here below.
Hunch or intuition: A hypothesis may be based simply on hunch or intuition of a person. It
is a sort of virgin idea. Such a hypothesis, if tested, may ultimately make an important
contribution to the existing science or body of knowledge. However, when a hypothesis is
tested in only one study, it suffers from two limitations. First, there is no assurance that the
relationship established between the two variables incorporated in the hypothesis will be
found in other studies. Secondly, the findings of such a hypothesis are likely to be unrelated
to, or unconnected with other theories or body of science. They are likely to remain isolated

9. bits of information. Nevertheless, these findings may raise interesting questions of worth
pursuing. They may stimulate further research, and if substantiated, may integrate into an
explanatory theory.
Findings of other: A hypothesis may originate from findings of other study or studies. A
hypothesis that rests on the findings of other studies is obviously free from the first limitation,
i.e. there is no assurance that it may relate with other studies. If such a hypothesis is proved,
it confirms findings of the earlier studies though it replicates earlier study conducted in
different concrete conditions.
A theory or a body of theory: A hypothesis may stem from existing theory or a body of
theory. A theory represents logical deductions of relationship between inter-related proved
facts. A researcher may formulate a hypothesis, predicting or proposing certain relationship
between the facts or propositions interwoven in a theory, for verifying or reconfirming the
relationship. A theory gives direction to research by stating what is known. Logical
deductions from these known facts may trigger off new hypotheses.
General social culture: General social culture furnishes many of its basic hypotheses.
Particular value-orientation in the culture, if it catches attention of social scientists for their
careful observation, generates a number of empirically testable propositions in the form of
hypotheses.
Analogy: Analogies may be one of the fertile sources of hypothesis. Analogies stimulate new
valuable hypotheses. They are often a fountainhead of valuable hypotheses. Even casual
observation in the nature or in the framework of another science may be a fertile source of
hypotheses. A proved particular pattern of human behavior, in a set of circumstances or social
settings, may be a source of hypothesis. A researcher may be tempted to test these established
co-relations with similar attributes in different social settings. Researcher may be interested
to test these analogies in a sort of different settings and circumstances. Researcher seeks
inspiration for formulating the hypothesis from analogies of others. However, a researcher,
when s/he uses analogy as a source of his/her hypothesis, needs to carefully appreciate the

10. theoretical framework in which the analogy was drawn and its relevancy in the new frame of
reference.
Personal experience: Not only do culture, science and analogy, among others, affect the
formulation of hypotheses. The way in which an individual reacts to each of these is also a
factor in the statement of hypotheses. Therefore, individual experience of an individual
contributes to the type and the form of the questions researcher asks, as also to the kinds of
tentative answers to these questions (hypotheses) that s/he might provide. Some scientists
may perceive an interesting pattern from merely seem a ‘jumble of facts’ to a common man.
The history of science is full of instances of discoveries made because the ‘right’ individual
happened to make the ‘right’ observation because of researcher particular life history,
personal experience or exposure to a unique mosaic of events. Researcher personal
experience or life history may influence his/her perception and conception and in turn direct
quite readily to formulate certain hypothesis.
Thus, a hypothesis may originate from a variety of sources, in isolation or in combination
with another. However, in spite of these fertile sources of hypotheses, it is not easy to
formulate a usable or workable hypothesis. It is often more difficult to find and formulate a
problem than to solve it. If a researcher succeeds in formulating a hypothesis, s/he can assure
that it is half-solved. While formulating a hypothesis, researcher has to keep reminding that
s/he has to formulate tentative proposition in such a way that it becomes usable in systematic
study.
TYPES OF RESEARCH HYPOTHESIS
Before researchers can begin working on a question that interests them, they need to formulate
a research hypothesis. This is an important step in the scientific method because this
determines the direction of the study. Scientists need to scrutinize previous work in the area
and select an experimental design to use that helps them find data that either supports or
rejects their hypothesis. Research hypotheses are of different types: simple, complex,

11. directional, non-directional, associative, causal, inductive & deductive, null, and alternative
or research.
Simple Hypothesis: This predicts the relationship between a single independent variable (IV)
and a single dependent variable (DV). For example: Lower levels of exercise postpartum (IV)
will be associated with greater weight retention (DV).
Complex Hypothesis: This predicts the relationship between two or more independent
variables and two or more dependent variables. Example of a complex multiple independent
variable hypothesis - low risk pregnant women (IV) who value health highly; believe that
engaging in health promoting behaviours will result in positive outcomes; perceive fewer
barriers to health promoting activities; are more likely than other women to attend pregnancy-
related education programs (DV). Another example of a complex multiple dependent variable
hypothesis - the implementation of an evidence based protocol for urinary incontinence (IV)
will result in (DV) decreased frequency of urinary incontinence episodes; decreased urine
loss per episode; decreased avoidance of activities among women in ambulatory care settings.
Directional Hypothesis: This may imply that the researcher is intellectually committed to a
particular outcome. They specify the expected direction of the relationship between variables
i.e. the researcher predicts not only the existence of a relationship but also its nature. Scientific
journal articles generally use this form of hypothesis. The investigator bases this hypothesis
on the trends apparent from previous research on this topic. Considering the example, a
researcher may state the hypothesis as, ‘High school students who participate in
extracurricular activities have a lower GPA than those who do not participate in such
activities.’ Such hypotheses provide a definite direction to the prediction.
Non-directional Hypothesis: This form of hypothesis is used in studies where there is no
sufficient past research on which to base a prediction. Do not stipulate the direction of the
relationship. Continuing with the same example, a non-directional hypothesis would read,
‘The academic performance of high school students is related to their participation in
extracurricular activities.’

12. Associative Hypothesis: Associative hypotheses propose relationships between variables,
when one variable changes, the other changes. Do not indicate cause and effect.
Causal Hypothesis: Causal hypotheses propose a cause and effect interaction between two
or more variables. The independent variable is manipulated to cause effect on the dependent
variable. The dependent variable is measured to examine the effect created by the independent
variable. For the example mentioned, the causal hypothesis will state, ‘High school students
who participate in extracurricular activities spend less time studying which leads to a low
GPA.’ When verifying such hypotheses, the researcher needs to use statistical techniques to
demonstrate the presence of a relationship between the cause and effect. Such hypotheses also
need the researcher to rule out the possibility that the effect is a result of a cause other than
what the study has examined.
Inductive and Deductive Hypotheses: Inductive hypotheses are formed through inductively
reasoning from many specific observations to tentative explanations. Deductive hypotheses
are formed through deductively reasoning implications of theory.
Null Hypothesis: This is a hypothesis that proposes no relationship or difference between
two variables. This is the conventional approach to making a prediction. It involves a
statement that says there is no relationship between two groups that the researcher compares
on a certain variable. The hypothesis may also state that there is no significant difference
when different groups are compared with respect to a particular variable. For example, ‘There
is no difference in the academic performance of high school students who participate in
extracurricular activities and those who do not participate in such activities’ is a null
hypothesis. It asserts that there is no true difference in the sample statistic and population
parameter under consideration (hence the word ‘null’ which means invalid, void, or a
mounting to nothing) and that the difference found is accidental arising out of fluctuations of
sampling. It is denoted as H0.
What is sampling?

13. Sampling definition: Sampling is a technique of selecting individual members or a subset of the
population to make statistical inferences from them and estimate characteristics of the whole
population. Different sampling methods are widely used by researchers in market research so that
they do not need to research the entire population to collect actionable insights. It is also a time-
convenient and a cost-effective method and hence forms the basis of any research design.
Sampling techniques can be used in a research survey software for optimum derivation.
For example, if a drug manufacturer would like to research the adverse side effects of a drug on
the country’s population, it is almost impossible to conduct a research study that involves
everyone. In this case, the researcher decides a sample of people from each demographic and
then researches them, giving him/her indicative feedback on the drug’s behavior.
Sample
A sample is a smaller, manageable version of a larger group. It is a subset containing the
characteristics of a larger population. Samples are used in statistical testing when population sizes
are too large for the test to include all possible members or observations. A sample should
represent the whole population and not reflect bias toward a specific attribute.
Characteristics of a Good Sample
(1) Goal-oriented: A sample design should be goal oriented. It is means and should be oriented
to the research objectives and fitted to the survey conditions.
(2) Accurate representative of the universe: A sample should be an accurate representative of
the universe from which it is taken. There are different methods for selecting a sample. It will be
truly representative only when it represents all types of units or groups in the total population in
fair proportions. In brief sample should be selected carefully as improper sampling is a source of
error in the survey.

14. (3) Proportional: A sample should be proportional. It should be large enough to represent the
universe properly. The sample size should be sufficiently large to provide statistical stability or
reliability. The sample size should give accuracy required for the purpose of particular study.
(4) Random selection: A sample should be selected at random. This means that any item in the
group has a full and equal chance of being selected and included in the sample. This makes the
selected sample truly representative in character.
(5) Economical: A sample should be economical. The objectives of the survey should be
achieved with minimum cost and effort.
(6) Practical: A sample design should be practical. The sample design should be simple i.e. it
should be capable of being understood and followed in the fieldwork.
(7) Actual information provider: A sample should be designed so as to provide actual
information required for the study and also provide an adequate basis for the measurement of its
own reliability.
In brief, a good sample should be truly representative in character. It should be selected at random
and should be adequately proportional. These, in fact, are the attributes of a good sample.
Essentials of a Sample Investigation

15. Some critical essentials of sampling include:
1. Representativeness – You must select the sample in a manner which represents the universe
in its truest sense. Further, if you fail to do so, then you might get misleading results.
2. Adequacy – You should also select the size of the sample adequately which represents the
parametric characteristics of the population.
3. Independence – When you select a sample, you must ensure that you select the items
independently and also randomly.
4. Homogeneity – This is another important element of a sample investigation. Homogeneity
means that there is no basic difference in the nature of the units in the sample and the universe.
Merits of a Sample Investigation
Here are some important merits of sampling:

16. 1. Cost-efficient – In a sample investigation, the costs associated with the collection of data are
less. This is because you collect data only from a fraction of the entire population. Therefore,
it is cost-efficient.
2. Time-efficient – In sampling, you require less time to collect, analyze, and interpret the data
since you are working only on a fraction of the population. Hence, it is time-efficient too.
3. Reliable – Usually, the data collected under a sample investigation is reliable because of the
use of well-trained and experienced investigators or experts.
4. Flexible – When you collect data through sampling, you have a greater scope of flexibility.
5. Detailed Information – Since sampling is cost-efficient and also time-efficient, you can
collect detailed information about the sample in your survey.
Demerits of a Sample Investigation
While sampling has many merits, there are some demerits associated with it too. Here is a quick
look:
1. It is impossible to attain a 100 percent accuracy using this process. This is because the
investigator draws conclusions about the characteristics of the population using the results
that he obtains from the selected sample.
2. The results are prone to a sampling error or a random error.
3. Experts are required to ensure that the results of a sample investigation are satisfactory.
4. Sometimes, the sample does not represent the population correctly. This is because it depends
on the attitude and mindset of the investigators.
5. If the population has a heterogeneous character, then you cannot use this method.
Types of sampling: sampling methods

17. Sampling in market research is of two types – probability sampling and non-probability sampling.
Let’s take a closer look at these two methods of sampling.
1. Probability sampling: Probability sampling is a sampling technique where a researcher sets
a selection of a few criteria and chooses members of a population randomly. All the members
have an equal opportunity to be a part of the sample with this selection parameter.
2. Non-probability sampling: In non-probability sampling, the researcher chooses members for
research at random. This sampling method is not a fixed or predefined selection process. This
makes it difficult for all elements of a population to have equal opportunities to be included in
a sample.
Types of probability sampling with examples:
Probability sampling is a sampling technique in which researchers choose samples from a larger
population using a method based on the theory of probability. This sampling method considers
every member of the population and forms samples based on a fixed process.
For example, in a population of 1000 members, every member will have a 1/1000 chance of
being selected to be a part of a sample. Probability sampling eliminates bias in the population
and gives all members a fair chance to be included in the sample.

18. There are four types of probability sampling techniques:
• Simple random sampling: One of the best probability sampling techniques that helps in
saving time and resources, is the Simple Random Sampling method. It is a reliable method of
obtaining information where every single member of a population is chosen randomly, merely
by chance. Each individual has the same probability of being chosen to be a part of a sample.
For example, in an organization of 500 employees, if the HR team decides on conducting team
building activities, it is highly likely that they would prefer picking chits out of a bowl. In this
case, each of the 500 employees has an equal opportunity of being selected.
• Cluster sampling: Cluster sampling is a method where the researchers divide the entire
population into sections or clusters that represent a population. Clusters are identified and
included in a sample based on demographic parameters like age, sex, location, etc. This makes
it very simple for a survey creator to derive effective inference from the feedback.
For example, if the United States government wishes to evaluate the number of immigrants
living in the Mainland US, they can divide it into clusters based on states such as California,
Texas, Florida, Massachusetts, Colorado, Hawaii, etc. This way of conducting a survey will be
more effective as the results will be organized into states and provide insightful immigration
data.
• Systematic sampling: Researchers use the systematic sampling method to choose the sample
members of a population at regular intervals. It requires the selection of a starting point for
the sample and sample size that can be repeated at regular intervals. This type of sampling
method has a predefined range, and hence this sampling technique is the least time-
consuming. For example, a researcher intends to collect a systematic sample of 500 people in
a population of 5000. He/she numbers each element of the population from 1-5000 and will
choose every 10th individual to be a part of the sample (Total population/ Sample Size =
5000/500 = 10).
• Stratified random sampling: Stratified random sampling is a method in which the researcher
divides the population into smaller groups that don’t overlap but represent the entire
population. While sampling, these groups can be organized and then draw a sample from each
group separately. For example, a researcher looking to analyze the characteristics of people
belonging to different annual income divisions will create strata (groups) according to the

19. annual family income. Eg – less than $20,000, $21,000 – $30,000, $31,000 to $40,000,
$41,000 to $50,000, etc. By doing this, the researcher concludes the characteristics of people
belonging to different income groups. Marketers can analyze which income groups to target
and which ones to eliminate to create a roadmap that would bear fruitful results.
Uses of probability sampling
There are multiple uses of probability sampling. They are:
• Reduce Sample Bias: Using the probability sampling method, the bias in the sample derived
from a population is negligible to non-existent. The selection of the sample mainly depicts the
understanding and the inference of the researcher. Probability sampling leads to higher
quality data collection as the sample appropriately represents the population.
• Diverse Population: When the population is vast and diverse, it is essential to have adequate
representation so that the data is not skewed towards one demographic. For example, if Square
would like to understand the people that could make their point-of-sale devices, a survey
conducted from a sample of people across the US from different industries and socio-economic
backgrounds helps.
• Create an Accurate Sample: Probability sampling helps the researchers plan and create an
accurate sample. This helps to obtain well-defined data.
Types of non-probability sampling with examples
The non-probability method is a sampling method that involves a collection of feedback based
on a researcher or statistician’s sample selection capabilities and not on a fixed selection process.
In most situations, the output of a survey conducted with a non-probable sample leads to skewed
results, which may not represent the desired target population. But, there are situations such as
the preliminary stages of research or cost constraints for conducting research, where non-
probability sampling will be much more useful than the other type.
Four types of non-probability sampling explain the purpose of this sampling method in a better
manner:

20. • Convenience sampling: This method is dependent on the ease of access to subjects such as
surveying customers at a mall or passers-by on a busy street. It is usually termed
as convenience sampling, because of the researcher’s ease of carrying it out and getting in
touch with the subjects. Researchers have nearly no authority to select the sample elements,
and it’s purely done based on proximity and not representativeness. This non-probability
sampling method is used when there are time and cost limitations in collecting feedback. In
situations where there are resource limitations such as the initial stages of research,
convenience sampling is used. For example, startups and NGOs usually conduct convenience
sampling at a mall to distribute leaflets of upcoming events or promotion of a cause – they do
that by standing at the mall entrance and giving out pamphlets randomly.
• Judgmental or purposive sampling: Judgmental or purposive samples are formed by the
discretion of the researcher. Researchers purely consider the purpose of the study, along with
the understanding of the target audience. For instance, when researchers want to understand
the thought process of people interested in studying for their master’s degree. The selection
criteria will be: “Are you interested in doing your masters in …?” and those who respond with
a “No” are excluded from the sample.
• Snowball sampling: Snowball sampling is a sampling method that researchers apply when the
subjects are difficult to trace. For example, it will be extremely challenging to survey shelter
less people or illegal immigrants. In such cases, using the snowball theory, researchers can
track a few categories to interview and derive results. Researchers also implement this
sampling method in situations where the topic is highly sensitive and not openly discussed—
for example, surveys to gather information about HIV Aids. Not many victims will readily
respond to the questions. Still, researchers can contact people they might know or volunteers
associated with the cause to get in touch with the victims and collect information.
• Quota sampling: In Quota sampling, the selection of members in this sampling technique
happens based on a pre-set standard. In this case, as a sample is formed based on specific
attributes, the created sample will have the same qualities found in the total population. It is a
rapid method of collecting samples.
Uses of non-probability sampling
Non-probability sampling is used for the following:

21. • Create a hypothesis: Researchers use the non-probability sampling method to create an
assumption when limited to no prior information is available. This method helps with the
immediate return of data and builds a base for further research.
• Exploratory research: Researchers use this sampling technique widely when conducting
qualitative research, pilot studies, or exploratory research.
• Budget and time constraints: The non-probability method when there are budget and time
constraints, and some preliminary data must be collected. Since the survey design is not rigid,
it is easier to pick respondents at random and have them take the survey or questionnaire.
How do you decide on the type of sampling to use?
For any research, it is essential to choose a sampling method accurately to meet the goals of your
study. The effectiveness of your sampling relies on various factors. Here are some steps expert
researchers follow to decide the best sampling method.
• Jot down the research goals. Generally, it must be a combination of cost, precision, or accuracy.
• Identify the effective sampling techniques that might potentially achieve the research goals.
• Test each of these methods and examine whether they help in achieving your goal.
• Select the method that works best for the research.
Difference between Probability Sampling and Non-Probability Sampling Methods
We have looked at the different types of sampling methods above and their subtypes. To
encapsulate the whole discussion, though, the significant differences between probability
sampling methods and non-probability sampling methods are as below:
Probability Sampling Methods
Non-Probability Sampling
Methods
Definition
Probability Sampling is a sampling
technique in which samples from a
larger population are chosen using a
method based on the theory of
probability.
Non-probability sampling is a
sampling technique in which the
researcher selects samples based
on the researcher’s subjective
judgment rather than random
selection.

22. Alternatively
Known as
Random sampling method. Non-random sampling method
Population
selection
The population is selected
randomly.
The population is selected
arbitrarily.
Nature The research is conclusive. The research is exploratory.
Sample
Since there is a method for deciding
the sample, the population
demographics are conclusively
represented.
Since the sampling method is
arbitrary, the population
demographics representation is
almost always skewed.
Time Taken
Takes longer to conduct since the
research design defines the selection
parameters before the market
research study begins.
This type of sampling method is
quick since neither the sample or
selection criteria of the sample are
undefined.
Results
This type of sampling is entirely
unbiased and hence the results are
unbiased too and conclusive.
This type of sampling is entirely
biased and hence the results are
biased too, rendering the research
speculative.
Hypothesis
In probability sampling, there is an
underlying hypothesis before the
study begins and the objective of
this method is to prove the
hypothesis.
In non-probability sampling, the
hypothesis is derived after
conducting the research study.