This document discusses hypotheses in research. It defines a hypothesis as a tentative statement about the relationship between two or more variables that can be tested. The key types of hypotheses discussed are simple vs complex, directional vs nondirectional, null vs research, and associative vs causal. Good characteristics of a hypothesis include being testable, consistent with existing knowledge, and having conceptual clarity. Hypotheses can be generated from theoretical frameworks, previous research, literature reviews, and real-life experiences. The steps of hypothesis testing include setting the null and research hypotheses, determining the test statistic and significance level, calculating the test statistic, and making a decision to accept or reject the null hypothesis based on the critical value. Type 1 and Type 2

1. HYPOTHESIS
Name-Ankit paI
Id-----21407--
Dept-Agrl.Extension
Institute-IARI, New
Delhi

2. INTRODUCTION
 hypothesis is a formal tentative statement of the
expected relationship between two or more
variables.
 A hypothesis helps to translate the research
problem & objectives into a clear explanation or
prediction of the expected results or outcomes of
the research study
 A clearly stated hypothesis includes the Variables
to be manipulated or measured, identifies the
population to be examined, & indicates the
proposed outcome for the study.

3. WHAT IS RESEARCH PROBLEM?
 A research problem is one which
requires a researcher to find out the
best solution for the given problem,
i.e., to find out by which course of
action that objective can be attained
properly.
LITERATURE REVIEW
literature review is an account of what
has been already established or published
on a particular research topic.
The main purpose of literature review is to
convey to the readers about the work
already done & the knowledge & ideas
that have been already established on a
particular topic of research.

4. Sources of Review of literature

5. Hypothesis Defined as...
 An educated guess•
 A tentative point of view.
 A proposition not yet tested•
 A preliminary explanation.

6. The word hypothesis is derived from the Greek word
"hypotithenai meaning ‘to put under’ or ‘to suppose’.
The word hypothesis consists of two words "Hypo' and
thesis.
Hypo' means tentative or subject to the verification
and"Thesis' means statement about solution of a problem.
So the word "Hypothesis" means tentative statement about
solution of a problem or Hypothesis means the guesses to
solve the research problem
Meaning of Hypothesis-

7. Various Authors also defined...
 "A hypothesis is a conjectural statement of the relation between
two or more variables". (Kerlinger, 1956)
 "Hypotheses are single tentative guesses, good hunches - assumed
for use in devising theory or planning experiments intended to be
given a direct experimental test when possible". (Eric Rogers, 1966)•
 "Hypothesis is a formal statement that presents the expected
relationship between an independent and dependent
variable."(Creswell, 1994)
 A hypothesis is a logical supposition, a reasonable guess, an
educated conjecture. It provides a tentative explanation for a
phenomenon under investigation." (Leedy and Ormrod, 2001)

8. Importance of hypothesis in
research
 Hypotheses enables the researcher to objectives investigate new
areas of discovery. Thus, it provides a powerful tool for the
advancement of knowledge.
 Hypotheses provides objectivity to the research activity.
 It also provides directions to conduct research such as defining the
sources & relevance of data.
 Hypotheses provides clear & specific goals to the researchers.
 These clear & specific goals provide the nvestigator with a basis for
selecting sample & research procedures to meet these goals.

9. • Hypotheses provides link between theories actual practical
research.
• It provides a bridge between theory & reality.
• A hypothesis suggests which type of research is likely to be most
appropriate.
• As it is a tentative statement of anticipated results, it guides the
researcher towards the direction in which the research should
proceed.
• It stimulates the thinking process of researcher as the researcher
forms the hypothesis by anticipating the outcome.

10. • It also determines the most appropriate research designs &
techniques of data analysis.
• Hypotheses provides understanding to the researchers about what
expect from the results of the research study.
• It serves as framework for drawing conclusions of a research study.
• Without hypotheses, research would be like inless wandering.

11. Characteristics of a good hypothesis
Conceptual clarity:
Hypothesis should consist of clear defined & understandable
concepts. It should be stated in very terms, the meaning &
implication of which cannot be doubted. To facilitate the
conceptual clarity, hypothesis can be stated in declarative
statement, in present tense.
Empirical referents:
Research must have an ultimate empirical referent. No
usable hypothesis can embody moral judgments. A good
hypothesis must have empirical basis from the area of
enquiry.

12. Objectivity:
Hypothesis must be objective, which facilitates objectivity in
data collection & keeps the research activity free from
researcher value judgment.
Specificity:
It should be specific, not general, & should explain the expected
relations between variables. For example, regular yoga reduces
stress.

13. Relevant:
The hypothesis should be relevant to the problem being
studied as well as the objectives of the study. Hypothesis must
have relevance with theory under test in a research process.
Testability:
Hypothesis should be testable & should not be a moral
judgment. It must be directly/indirectly observable &
measurable. The researcher can set up a situation that
permits one to assess if it is true or false. It must be
verifiable.

14. Consistency
A hypothesis should be consistent with an existing body of
theories, research findings, & other hypotheses. It should
correspond with existing knowledg.
Simplicity:
A hypothesis should be formulated in simple &
understandable terms. It should require fewer conditions
& assumptions.

15. Availability of techniques:
The researchers must make sure that
methods are available for testing their
proposed hypotheses.
Purposiveness:
The researcher must formulate only
purposeful hypotheses, which has
relevancewith research problem &
objectives.
Verifiability:
A good hypothesis can be actually verified
in practical terms.

16. Profundity of effect:
A good hypothesis should have profound
effect upon a variety of research variables.
Economical:
The expenditure of money & the time can be
controlled if the hypotheses underlying the
research undertaken is good.

17. Sources of Hypothesis
1. Theoretical or conceptual frameworks .
2. Real-life experiences.
3. Previous research.
4. Academic literature.

18. 1. Theoretical or conceptual
frameworks:
The most important sources of
hypothesis are theoretical or conceptual
frameworks developed for the
studyThrough a deductive approach.
these hypotheses are drawn from
theoretical or conceptual frameworks
for testing them.

19. 2.Previous research:
Findings of the previous studies may be used for framing the
hypotheses for another study.•
3.Real-life experiences:•
Real-life experiences also contribute the
formulation of hypotheses for research studies.•
For example, Newton had a life-changing
experience of the falling of an apple & formulated
a hypothesis that earth attracts all the mass
towards its centre.

20. 4.Academic literature
 Academic literature is based on formal theories,
empirical evidences, experiences, observation, &
conceptualizations of academic.
 These literatures may serve as good sources for
Formulating hypotheses for research studies.

21. Types of Hypotheses
 Simple Hypothesis
 complex hypothesis
 Question form of hypothesis
 Directional & nondirectional hypothesis
 Null hypothesis
 Research hypothesis
 Associative hypothesis
 Causal hypothesis

22. Simple & complex hypothesis
 Simple hypothesis:It is a statement which reflects the relationship
between two variables.For example, the lower the level of
hemoglobin, the higher is the risk of infection among postpartum
women.
 Complex hypothesis:It is a statement which reflects the relationship
between more than two variables.For Example, 'satisfaction is higher
among patients who der & dwelling in rural area than those who are
younger & dwelling in urban area'.
QUESTION FORM OF HYPOTHESIS:
• It Is the simplest form of empirical hypothesis.
• In simple case of investigation and research are
adequately implemented by resuming a question•
• Ex. how is the ability of 9th class students in learning
moral values?

23. Associative & causal hypothesis
Associative hypothesis:
 It reflects a relationship between variables that occurs exists in
natural settings without manipulation.
 This hypothesis is used in correlational research studies.
Causal hypothesis
 It predicts the cause and effect relationship between two or more
dependent & independent variables in experimental or interventional
setting, where independent variable is manipulated by research to
examine the effect on the dependent variable.
 The causal hypothesis reflects the measurement of dependent
variable to examine the effect of dependent variable, which is
manipulated by the researcher(s).

24. Directional & nondirectional
hypothesis
Directional hypothesis
 It specifies not only the existence, but also the expected
direction of the relationship between variables.
 Directional hypothesis states the nature of the
relationship between two or more variables such as
positive, negative, or no relationship.
 Example- if student does continue smart and hard work,
definitely he/she will achieve a goal.

25. Nondirectional Hypothesis:
 It reflects the relationship between
two or more variables, but is does not
specify the anticipated direction &
nature of relationship such as positive
or negative.
 It indicates the existence of
relationship between the variables.
 Example- relationship between the
teacher and student influce learning of
student.

26. Null & research hypothesis
 Null hypothesis (Ho):
 It is also known as statistical hypothesis & is used for
statistical testing & interpretation of statistical outcomes.
 It states the existence of no relationship between the
independent & dependent variables.
 Research hypothesis (H1):
 Also known as Alternate hypothesis.
 states the existence of relationship between two or more
variables.

27. Testing of Hypothesis
 A hypothesis is an assumption about the population
parameter (say population mean) which is to be
tested.
 For that we collect sample data, then we calculate
sample statistics (say sample mean) and then use
this information to judge/decide whether
hypothesized value of population parameter is
correct or not.
• To test the validity of assumed or hypothetical value of
population, we gather sample data and determine the
difference between hypothesized value and actual value of
the sample mean.•

28. • Hypothesis testing
• Then we judge whether the difference is
significantor not.
• The smaller the difference, the greater the
likelihood that our hypothesized value for the
mean is correct.
• The larger the difference, the smaller the
likelihood.
• In hypothesis testing the first step is to state
the assumed hypothesized( numerical) value of
the population parameter. The assumption we
wish/ want to test is called the null hypothesis.
The symbol for null hypothesis is Ho

29. Procedure of Hypothesis Testing
The Hypothesis Testing comprises the following steps:
STEP 1
Setting up the hypothesis.
STEP 2
Selection of significance level.
 The confidence with which an experimenter rejects or accepts
Null Hypothesis depends on the significance level adopted.
 Level of significance is the rejection region ( which is outside
the confidence or acceptance region). The level of significance,
usually denoted by the alfa.

30. Selecting a significance level
 Though any level of significance can be adopted,
in practice we either take 5% or 1% level of
significance.
 When we take 5% level of significance(a= .05),
then there are about 5 chances out of 100 that we
would reject the null hypothesis.
 In other words out of 100, 95% chances are there
that the null hypothesis will be accepted i.e. we
are about 95% confident that we have made the
right decision

32. • If our sample statistic(calculated value) fall in the non
shaded region acceptance region), then it simply
means that there is no evidence to reject the null
hypothesis.
• It proves that null hypothesis (H) is true. Otherwise, it
will be rejected.
Step 3
Determination of suitable test statistic:
For example Z, t, Chi-Square or F-statistic.
It will be decide according to the condtion
Step 4
Determine the critical value from
the table.

33. Step 5
check the sample result.
Compare the calculated value( sample result) with the
value obtained from the table (tabulated or critical
value)
Step 6
Making Decisions
• Making decisions means either accepting or rejecting
the null hypothesis.
• If computed value (absolute value) is more than the
tabulated or critical value, then it falls in the critical
region. In that case, reject null hypothesis, otherwise
accept.

34. Type I and Type II Errors
When a statistical hypothesis is tested, there are 4
possible results:
(1)The hypothesis is true but our test accepts it.
(2) The hypothesis is false but our test rejects it.
(3)The hypothesis is true but our test rejects it.
(4) The hypothesis is false but our test accepts it.
Obviously, the last 2 possibilities lead to errors.
 Rejecting a null hypothesis when it is true is
called a Type I error.
 Accepting a null hypothesis when it is false is
called Type II error.

36. One-Tailed and Two-TailedTests
 Two-Tailed Test is that where the hypothesis about the
population parameter is rejected for the value of sample statistic
failing into either tail of the distribution.(fig3)
 When the hypothesis about the population parameter is rejected
for the value of sample statistic failing into one side tail of the
distribution, then it is known as one-tailedtest.
 If the rejection area falls on the Two-Tailed Test is that where
the hypothesis about the population parameter is rejected for
the value of sample statistic failing into either tail of the
distribution.right side, then it is called right-tailed test.(fig 2)
 On the other hand If the rejection area falls on the right side,
then it is called left-tailed test.(fig 1)

39. Power of test (1-B)
 It measures how well the test is working.
 Power of rejecting Ho hypothesis , when it’s false.
 Power of accepting the (Ho) When it’s true
 If 1-B very much closer to the unity(I.e nearer
1.0) then we can infer that test is working well,
means test is rejecting when Ho is false.
 If (1-B) very nearer to 0.0,we infer that test is
working poorly.

40. Limitations of Test of Hypothesis
 Important limitations are as follows:
 Testing is not decision-making itself; the tests are only useful aids
for decision-making.
 "proper interpretation of statistical evidence is important to
intelligent decisions.
 "Test do not explain the reasons as to why does the difference
exist, viz. between the means of the two samples.
 They simply indicate whether the difference is due to
fluctuations of sampling or because of other reasons•
 The tests do not tell us the reasons causing the difference.

41. • Results of significance tests are based on probabilities and
as such cannot be expressed with full certainty..
• Statistical inferences based on the significance tests
cannot be said to be entirely correct evidences concerning
the truth of the hypotheses.
• This is specially so in case of small samples where the
probability of drawing erring inferences happens to be
generally higher.
• For greater reliability, the size of samples be sufficiently
enlarged. Sources– C. R kothari