Hypothesis

Hypothesis
DR. ANKITA CHATURVEDI

Hypothesis (plural Hypotheses)
 Hypothesis is the possible statement of a proposition or a reasonable guess, based upon the available
evidence, which the researcher seeks to prove through his study.
 Hypothesis or Hypotheses are defined as the formal statement of the tentative or expected prediction or
explanation of the relationship between two or more variables in a specified population.
 It is a tentative guess or a statement which is to be proved.
 Hypothesis reflect the research worker’s guess as to the probable outcome of the experiments.

Hypothesis (plural Hypotheses)
 A hypothesis helps to translate the research problem and objective into a clear explanation or
prediction of the expected results or outcomes of the study.
 Hypothesis is derived from the research problems, literature review and conceptual framework.
Hypothesis in a research project logically follow literature review and conceptual framework.

Hypothesis makes the following
contributions in research study
 It provides clarity to the research problem and research objectives
 It describes, explains or predicts the expected results or outcome of the research.
 It indicates the type of research design.
 It directs the research study process.
 It identifies the population of the research study that is to be investigated or examined.
 It facilitates data collection, data analysis and data interpretation
It helps in directing the inquiries in the right directions.

Characteristics of A Good Hypothesis
CONCEPTUAL CLARITY
• A good hypothesis consists of clearly defined and understandable concepts.
• It is stated in a very clear terms, the meaning and implications of which cannot be
doubted.

Characteristics
SPECIFIC
• A good research hypothesis must be specific, not general and should explain the expected
relations between variables .
RELEVANT
 The hypothesis should be relevant to the problem and objectives under enquiry.
In addition hypothesis must have relevance with a theory under test in a research process.
A researcher must know about the workable techniques before formulating a hypothesis.

Characteristics
TESTABILITY
• Hypothesis should be testable and should not be a moral judgment.
• It should be directly or indirectly measurable.
It must be verifiable. • E.g., a hypothesis such as “bad parents produce bad children”.,
cannot be tested.
A testable hypothesis clearly states the manipulatable independent variables and
measurable dependent variables in specific population which provides a clear idea about
an interventional protocol and whether it will be implemented precisely and consistently
as a treatment in the study.

Characteristics
CONSISTENCY
• A hypothesis should be consistent with an existing body of theories, research findings
and other hypothesis.
• It should correspond with existing knowledge.
SIMPLICITY
• A hypothesis should be formulated in simple and understandable terms.
• It should require fewer conditions and assumptions.

Characteristics
PURPOSIVE
 The researcher must formulate only purposeful hypothesis.
 Purposiveness refers to the relevance of hypothesis to the research problem and its objectives.
INTERNAL HARMONY
Internal harmony is a major characteristic of good hypothesis.
It should be out of contradictions and conflicts.
There must be a close relationship between variables which one is dependent on other.

Types of Hypothesis
There are eight forms of hypothesis and they are:
•Simple hypothesis
•Complex hypothesis
•Directional hypothesis
•Non-directional hypothesis
•Associative Hypothesis
•Casual hypothesis
•Null hypothesis
Alternative Hypothesis

Simple Hypothesis
It shows a relationship between one dependent variable and a single independent variable. For
example –
If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an
independent variable, while losing weight is the dependent variable.
Smoking leads to cancer
The higher ratio of unemployment leads to crimes.
Simple Hypothesis / Complex Hypothesis

Simple Hypothesis / Complex Hypothesis
Complex Hypothesis
It shows the relationship between two or more dependent variables and two or more
independent variables.
For Example: Eating more vegetables and fruits leads to weight loss, glowing skin,
reduces the risk of many diseases such as heart disease, high blood pressure and some
cancers.
Smoking and other drugs leads to cancer, tension, chest infections etc.
 The higher ration of unemployment poverty illiteracy leads to crimes like dacoit etc.

Directional Hypothesis/ Non-directional Hypothesis
Directional Hypothesis
It shows how a researcher is intellectual and committed to a particular outcome. The
relationship between the variables can also predict its nature. Directional Hypothesis
predicts the direction of the relationship between the independent and dependent variable.
For example-
 children aged four years eating proper food over a five-year period are having higher IQ
levels than children not having a proper meal. This shows the effect and direction of effect.
High quality of nursing education will lead to high quality of nursing practice skills.
Girls ability of learning moral science is better than boys.

Directional Hypothesis/ Non-directional Hypothesis
Non-directional Hypothesis
It is used when there is no theory involved. It is a statement that a relationship exists
between two variables, without predicting the exact nature (direction) of the relationship.
Non -directional Hypothesis predicts the relationship between the independent variable
and the dependent variable but does not specific the directional of the relationship.
 teacher student relationship influence student’s learning.
 There is no significant difference between 9th class boys and girls abilities of learning
moral values.

Associative Hypothesis/ Causal Hypothesis
Associative Hypothesis:
Reflects a relationship between variables that occurs or exists in natural settings without
manipulation.
This hypothesis is use in correlational research studies.
Causal Hypothesis
Predicts the cause-and-effect relationship between two or more dependent and
independent variables in experimental or interventional setting, where independent
variable is manipulated by researcher to examine the effect on dependent variable.

Null Hypothesis/ Alternative Hypothesis
Null Hypothesis
The null hypothesis is generally symbolized as H0 and the alternative hypothesis as Ha or H 1
It provides the statement which is contrary to the hypothesis. It’s a negative statement, and there is
no relationship between independent and dependent variables.
The hypothesis, “There is no significant difference between the academic achievement of high
school athletes and that of non athletes,”
“There is no significant effect of vaccine on prevention of small pox” are examples of null
hypothesis.
Since null hypotheses can be tested statistically, they are often termed as statistical hypotheses.

Alternative Hypothesis
The alternative hypothesis is a statement used in statistical inference experiment. It is
contradictory to the null hypothesis and denoted by Ha or H1.
We can also say that it is simply an alternative to the null.
In hypothesis testing, an alternative theory is a statement which a researcher is testing.
This statement is true from the researcher’s point of view and ultimately proves to reject
the null to replace it with an alternative assumption.
In this hypothesis, the difference between two or more variables is predicted by the
researchers, such that the pattern of data observed in the test is not due to chance.

Types of Alternative hypothesis
Basically, there are three types of the alternative hypothesis, they are;
Left-Tailed: Here, it is expected that (population mean) µ is less than a specified value say
50, such that;
H1 : µ < 50
Right-Tailed: It represents that µ is greater than some value, say 50.
H1 : µ > 50
Two-Tailed: According to this hypothesis, µ is not equal to a specific value say 50.
H1 : µ ≠ 50
Note: The null hypothesis for all the three alternative hypotheses, would be H0 : µ = 50.

Hypothesis:
Source
Theoretical
Framework
Previous
Research
Real Life
Experiences
Academic
Literature

Hypothesis- Source
THEORITICAL FRAMEWORK
Theoretical framework or conceptual framework are the most important sources of hypothesis.
Through a deductive approach these hypothesis are drawn for testing them.
PREVIOUS RESEARCH
Findings or previous research may be used for framing the hypothesis for a new study that have an area of relevance.
REAL-LIFE EXPERIENCES
• Real life experiences may contribute in the formulation of hypothesis.
• Newton had life changing experience of the falling of an apple and formulated the hypothesis that the earth attracts all the mass towards its center
before generating a law of gravity.
ACADEMIC LITERATURE
Academic literature is based on formal theories, empirical evidences, experiences, observations and conceptualizations of academicians.
These literatures may serve as good source for formulating hypothesis for research studies

Level of significance
This is very important concept in the context of hypothesis testing. It is always some percentage (usually
5% or 1 %) which should be chosen with great care, thought and reason.
In case we take the significance level at 5 per cent, then this implies that H0 will be rejected when the
sampling result (i.e. observed evidence) has a less than 0.05 probability of occurring if H0 is true.
In other words, the 5 percent level of significance means that researcher is willing to take as much as a 5
percent risk of rejecting the null hypothesis when it (H0) happens to be true.
Thus the significance level is the maximum value of the probability of rejecting H0 when it is true and is
usually determined in advance before testing the hypothesis.
It is denoted by alpha symbol (α)

Two-tailed and One-tailed tests
Two-Tailed Hypothesis Tests
Two-tailed hypothesis tests are also known as
nondirectional and two-sided tests because you can
test for effects in both directions. When you
perform a two-tailed test, you split the significance
level percentage between both tails of the
distribution. In the example below, I use an alpha
of 5% and the distribution has two shaded regions
of 2.5% (2 * 2.5% = 5%).

One-Tailed Hypothesis Tests
When a research study predicts a specific direction
for the treatment effect (increase or decrease), it is
possible to incorporate the directional prediction
into the hypothesis test.
One-tailed hypothesis tests are also known as
directional and one-sided tests because you can
test for effects in only one direction. When you
perform a one-tailed test, the entire significance
level percentage goes into the extreme end of one
tail of the distribution.

Errors in Hypothesis Tests
Just because the sample mean (following treatment) is different from the original population mean
does not necessarily indicate that the treatment has caused a change.
Because the hypothesis test relies on sample data, and because sample data are not completely
reliable, there is always the risk that misleading data will cause the hypothesis test to reach a
wrong conclusion.
Two types of error are possible.

Type I Errors
A Type I error occurs when the sample data appear to show a treatment effect when, in
fact, there is none.
In this case the researcher will reject the null hypothesis and falsely conclude that the
treatment has an effect.
Type I errors are caused by unusual, unrepresentative samples. Just by chance the
researcher selects an extreme sample with the result that the sample falls in the critical
region even though the treatment has no effect.
The hypothesis test is structured so that Type I errors are very unlikely; specifically, the
probability of a Type I error is equal to the alpha level.

Type II Errors
A Type II error occurs when the sample does not appear to have been affected by the
treatment when, in fact, the treatment does have an effect.
In this case, the researcher will fail to reject the null hypothesis and falsely conclude that
the treatment does not have an effect.
Type II errors are commonly the result of a very small treatment effect. Although the
treatment does have an effect, it is not large enough to show up in the research study.

ERRORS IN TESTING OF HYPOTHESIS
Decision
Accept H0 Reject H0
H0 (true) Correct decision Type I error (alpha error)
H0 (false) Type II error (ß error) Correct decision

Symbols used
Population Sample
Mean µ x
Standard Deviation σ s
Size N n

Hypothesis

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