This document provides information on hypotheses in research. It defines a hypothesis as a tentative statement proposed to explain certain facts or observations. A hypothesis should be specific, testable, and stated in advance of data collection. Hypotheses can be categorized as null or research hypotheses based on their formulation. They can also be directional or non-directional based on whether they specify an expected direction of results. Hypotheses are either deductively or inductively derived depending on whether they are tested top-down from existing theory or built bottom-up from observations.

1. Course Instructor
Dr. Rupasi Tiwari
Pr. Scientist and Incharge ATIC
ICAR-Indian Veterinary Research Institute
Izatnagar, UP-243122, India

2. HYPOTHESIS:
After the problem has been formulated, the hypothesis is derived
from the problem.
• It is a tentative answer to the research question or an
educated guess of the research outcome.
• A hypothesis is a conjectural statement of the relation
between two or more variables.
• It is a tentative answer to the research question or an
educated guess of the research outcome.
•
• It indicates the expectations of the researcher regarding
certain variables.
• It is the most specific way in which an answer to a problem
can be stated.

3. HYPOTHESIS:
greek -hypotithenai
meaning "to put under" or "to suppose.
 A hypothesis is a logical supposition, a
reasonable guess, an educated
conjecture.
 A hypothesis states what we are
looking for and it is a proposition
which can be put to a test to
determine it’s validity.

4. Definitions :
 A hypothesis may be precisely defined as a
tentative proposition suggested as a solution
to a problem or as an explanation of some
phenomenon. (Ary , Jacobs and Razavieh ,
1984)
 A hypothesis is a conjectural statement of the
relation between two or more variables.
(Kerlinger, 1956)
 Hypothesis is a formal statement that
presents the expected relationship between
an independent and dependent variable.
(Creswell, 1994)

5. Definitions :
 A hypothesis is a tentative generalization, the
validity of which remains to be tested. In its most
elementary stage, the hypothesis may be any
hunch guess, imaginative idea which becomes the
basis for action for action or investigation.
(Lundberg, G.A.)
 Hypothesis as a proposition stating in a testable
form the relationship between two or more
variables, and a conjectural statement of the
relationship between two or more variables.
Onyeogi (2000)
 Hypothesis is a statement postulating a possible
relationship between two or more phenomena or
variables. Mouton's (1990)

6. Hypothesis
 It is tentative statement: May be generalization about social
phenomena. The generalization is unknown, the validity of
the statement is unknown, it is never correct or wrong. You
may either support the statement or refute it.
 It is sometimes called a hunch, guess or an intuition it
indicates that with your own intuition hypothesis can be
formulated.
◦ Intution= comes from with in that’s likely to happen
◦ Hunch= a floating idea
◦ Guess=Anticipation
 It is a preposition. You propose it based on something
 Hypothesis aims at explaining casual relationships. It is in
fact an indispensable tool of the research process and
enables one to restrict and streamline one‘s search for the
ultimate solution to the research problem under investigation.

7. Salient differences between research
problem and hypothesis
Research Problem Hypothesis
Problem is a question and is not
testable
Hypotheses can be tested
It is the origin of hypothesis It is derived from problem
It is the problem of research It is the suggested solution
Relation between variables in
problem statements
Relation between variables in
hypotheses
Is A related to B? If A, then B.
How are A and B related to C? If A & B then C.
How is A related to B under
conditions C and D?
If A, then B under conditions C and
D.

8. Sources:
 Professional journals
 Creative thinking
 Conversation and discussion with peer & senior
researchers.
 Deduction from theories
 Experience of researcher himself
 Review of literature
 Pilot studies
 Based on chance- intuition
 Findings of another study
 One's own experience.
 Study and review of research literature pertaining to
the problem..Books, journals, theses directories,
encyclopedia etc..

9. Generation of research
hypothesis:Initial ideas
(undeveloped
)
Literature
review
Initial
observation
s
Problem
statement
Research
hypothesis
Operational
definitions of
constructs

10. Importance :
 It forms the starting point of investigation.
 It makes observation and experiment possible.
 It gives points to enquiry and helps in
deciding the direction in which to proceed.
 It helps in selecting pertinent facts.
 It offers explanations for the relationships
between those variables that can be
empirically tested.
 It furnishes proof that the researcher has
sufficient background knowledge to enable
him/her to make suggestions in order to
extend existing knowledge.
 It guides the research.

11. Importance
 It provides the investigator with a relational statement that is
directly testable in a research study.
 The hypothesis helps an investigator to collect the right kinds of
data needed for the investigation.
 It provides a framework for reporting conclusions of the study.
 It ensures the optimal use of researcher‘s valuable time and
other scarce resources by limiting the scope of the inquiry.
 It transforms research questions into testable propositions.
 It suggests the most appropriate methods and tools for the
analysis of data.
 In fact, conclusions are direct response to the hypothesis
formulated for the study as confirmed or discontinued by data
analysis.

12. Characteristics of Hypothesis

13. 1. Simple, understandable
terms
 A simple hypothesis contains one predictor and one outcome
variable, e.g. positive family history of schizophrenia increases
the risk of developing the condition in first-degree relatives.
Here the single predictor variable is positive family history of
schizophrenia and the outcome variable is schizophrenia.
 A complex hypothesis contains more than one predictor
variable or more than one outcome variable, e.g., a positive
family history and stressful life events are associated with an
increased incidence of Alzheimer’s disease.
 Here there are 2 predictor variables, i.e., positive family history
and stressful life events, while one outcome variable, i.e.,
Alzheimer’s disease.
 Complex hypothesis like this cannot be easily tested with a
single statistical test and should always be separated into 2 or
more simple hypotheses.

14. 2. It should be specific
 A specific hypothesis leaves no ambiguity about the subjects
and variables, or about how the test of statistical significance
will be applied.
 It uses concise operational definitions that summarize the
nature and source of the subjects and the approach to
measuring variables (History of medication with tranquilizers,
as measured by review of medical store records and
physicians’ prescriptions in the past year, is more common in
patients who attempted suicides than in controls hospitalized
for other conditions).
 This is a long-winded sentence, but it explicitly states the
nature of predictor and outcome variables, how they will be
measured and the research hypothesis. Often these details
may be included in the study proposal and may not be stated
in the research hypothesis. However, they should be clear in
the mind of the investigator while conceptualizing the study.

15. 3. Hypothesis should be
stated in advance
 The hypothesis must be stated in writing during the
proposal state.
 This will help to keep the research effort focused
on the primary objective and create a stronger
basis for interpreting the study’s results as
compared to a hypothesis that emerges as a result
of inspecting the data.
 The habit of post hoc hypothesis testing (common
among researchers) is nothing but using third-
degree methods on the data (data dredging), to
yield at least something significant.
 This leads to overrating the occasional chance
associations in the study.

16. Other Characteristics
4. It should have elucidating power and should be able to
furnish an acceptable explanation of the phenomenon.
5. It must be capable of empirical testing
6. It should be consistent with relevant objectives of research
and must be stated in a manner which provides direction for
the research.
7. A Hypothesis must be conceptually clear - concepts should
be clearly defined - the definitions should be commonly
accepted - the definitions should be easily communicable
8. The hypothesis should have empirical reference - Variables
in the hypothesis should be empirical realities - If they are
not it would not be possible to make the observation and
ultimately the test

17. Other Characteristics
 A hypothesis should be related to available
techniques of research - Either the techniques are
already available or - The researcher should be in
a position to develop suitable techniques
 The hypothesis should be related to a body of
theory - Hypothesis has to be supported by
theoretical argumentation - It should depend on the
existing body of knowledge. In this way the study
could benefit from the existing knowledge and later
on through testing the hypothesis could contribute
to the reservoir of knowledge

18. Some examples of hypothesis
 The productivity and production level in the poultry farms is
poor due to unawareness about improved methods.
 There exists in the country improved methods of poultry
production which, if used by the producers would increase
their profit.
 Farmers have not adopted new methods because they are
unaware of their existence.
 Farmers are unable to obtain new technologies due to
financial limitations.
 Special credit sources are necessary if farmers are to adopt
improved methods of production
 Farmers are unable to obtain credit which limit their ability to
finance changes in production methods.
 Egg price stabilization programme could induce farmers to
adopt improved methods of production.

19. Categorizing Hypotheses
Can be categorized in different ways
1. Based on their formulation
: Null Hypotheses and Alternate Hypotheses
2. Based on direction:
Directional and Non-directional Hypothesis
3. Based on their derivation
Inductive and Deductive Hypotheses

20. Categorizing Hypotheses
(Cont…)
 1. Null Hypotheses and Alternate Hypotheses
 Null hypothesis always predicts that no differences
between the groups being studied (e.g.,
experimental vs. control group) or no relationship
between the variables being studied
 By contrast, the alternate hypothesis always
predicts that there will be a difference between the
groups being studied (or a relationship between the
variables being studied)

21. Null Hypothesis
• It is a concise way to express the testing of obtained
data against chance expectations.
• The null hypothesis is a proposition that stipulates that
there would be no relationship or difference between
the variables being studied and that any such
relationship or difference if found to exist does so
accidentally or as a result of chance.
• This is reverse of research hypothesis.
• Null Hypothesis is used against research hypothesis
with a way to approve it or reject it.
• It is a hypothesis of no difference. Null mean zero,
when a hypothesis is stated negatively.

22. Null Hypothesis
• The objective of the hypothesis is to avoid the personal bias of
the investigator in the matter of collection of data.
• A null hypothesis is used to collect additional support for the
known hypothesis.
• The standard error is a means of testing the null hypothesis. It
expresses the null hypothesis since it is a measure of expected
chance fluctuation around the mean zero.
• It is very useful tool in testing the significance of difference. In its
simplest form the hypothesis asserts that there is no real
difference in the sample and the population in the particular
matter under consideration.
For example a null hypothesis : “There is no difference in the milk
hygiene among the farms of livestock owners who have undergone
the clean milk production trainings than those who have not attended
such trainings”

23. Example:
 H0 : 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.
 H1:The academic performance of high
school students is related to their
participation in extracurricular
activities.

24. Research Hypothesis
• This is called Working Hypothesis or Specific Hypothesis .
• Any hypothesis which is complementary to the null hypothesis is
called an alternative hypothesis and is usually denoted by H1.
• Such hypothesis are the statement about reality of things and
their realities are derived from theories and these pertain to
social phenomena.
• These specify the researcher‘s expectation of his/her empirical
test.
• These indicate that a relationship or difference exists between
two variables or groups and goes further to state the nature or
direction of that relationship or differences. These are the
hypothesis which can be confirmatory or non-confirmatory you
have to test it.
• These could be of two types: Directional and non directional.

25. Categorizing Hypotheses
(Cont…)
Alternate Hypothesis can further be classified as
2. Directional Hypothesis and Non-directional
Hypothesis
 Simply based on the wording of the hypotheses we can tell
the difference between directional and non-directional
 If the hypothesis simply predicts that there will be a difference
between the two groups, then it is a non-directional
hypothesis.
 It is non-directional because it predicts that there will be a
difference but does not specify how the groups will differ.
 If, however, the hypothesis uses so-called comparison terms,
such as “greater,”“less,”“better,” or “worse,” then it is a
directional hypothesis.
 It is directional because it predicts that there will be a
difference between the two groups and it specifies how the

26. Directional /Non Directional
Hypothesis
• For example :If A is valid, B follows ... or If you shout in the class the
teacher will punish you or Trainings wherein the teacher is more of a
facilitator and friendly leads to higher learning among the trainees than
those wherein the teacher is not friendly.
• These same hypothesis can be stated in a non directional manner.
• There will be a significant difference between the learning levels of
trainees who have attended trainings with a friendly teacher and those
who have attended trainings with a not so friendly one.
• Another hypothesis could be that “Milk hygiene in the farms of livestock
owners who have undergone the clean milk production trainings is
different than those who have not attended such trainings”

27. Example
 Directional :
 specifies the direction of expected
findings
 Eg: Students with high IQ will exhibit
more anxiety than students with low IQ”
 Non-directional:
 no definite direction of the expected
findings is specified.
 Eg: There is a difference in the anxiety
level of the children of high IQ and those
of low IQ.”

28. Categorizing Hypotheses
(Cont…)
 Inductive and Deductive
 Hypotheses (Theory Building and Theory Testing)
classified in terms of how they were derived:-
 Inductive hypothesis - a generalization based on
observation-
Observation-Pattern-Hypothesis-Theory
 Deductive hypothesis - derived from theory
Theory-Hypothesis-Observation-Confirmation

29. Deductive and Inductive
procedure
Deductive Inductive
1. Deductive reasoning happens
when a researcher works from
the more general information to
the more specific.
2. Sometimes this is called the “top-
down” approach. Because the
researcher starts at the top with a
very broad spectrum of
information and they work their
way down to a specific
conclusion.
3. For instance, a researcher might
begin with a theory about his or
her topic of interest. From there,
he or she would narrow that down
into more specific hypotheses
that can be tested
1. Inductive reasoning works the
opposite way, moving from
specific observations to
broader generalizations and
theories.
2. This is sometimes called a
“bottom up” approach.
3. The researcher begins with
specific observations and
measures, begins to then
detect patterns and
regularities, formulate some
tentative hypotheses to
explore, and finally ends up
developing some general
conclusions or theories.

30. Deductive and Inductive
procedure
Deductive
The hypotheses are then narrowed
down even further when
observations are collected to test
the hypotheses.
This ultimately leads the researcher
to be able to test the hypotheses
with specific data, leading to a
confirmation (or not) of the original
theory and arriving at a conclusion.

31. Deductive and Inductive
procedure
Deductive Inductive
An example of deductive reasoning
can be seen in this set of
statements:
Every day, I leave for work in my
car at eight o’clock.
Every day, the drive to work takes
45 minutes I arrive to work on time.
Therefore, if I leave for work at
eight o’clock today, I will be on
time.
An example of inductive reasoning
can be seen in this set of
statements:
Today, I left for work at eight o’clock
and I arrived on time.
Therefore, every day that I leave
the house at eight o’clock, I will
arrive to work on time.

32. Deductive and Inductive
procedure
Theory Theory
↓ ↑
Hypothesis Hypothesis
↓ ↑
Observation Pattern
↓ ↑
Confirmation Observation
Deduction Reasoning Induction Reasoning
Inductive reasoning is open-ended and
exploratory especially at the beginning.
Newton reached to "Law of Gravitation"
from "apple and his head” observation").
In a conclusion, when we use Induction
we observe a number of specific
instances and from them infer a general
principle or law.
Deductive reasoning is
narrow in nature and is
concerned with testing or
confirming hypothesis.

33. Problems in Formulating
Hypothesis
There are sometimes problem in formulation
hypothesis. This may be due to:
• Clear absence of theoretical framework
• Lack of ability of researcher
• Research Techniques: Researcher is not
familiar with the research technique or not
well acquainted to the available research
techniques.

34. Basics of hypothesis testing:
• Hypothesis testing or significance testing is
a method for testing a claim or hypothesis
about a parameter in a population, using
data measured in a sample.
• It involves:
• Formulation of hypothesis
• setting up a criterion for decision
• determining the test statstic
• making the decision

35. Example
 A hypothesis (for example, Tamiflu [oseltamivir], drug of choice in
H1N1 influenza, is associated with an increased incidence of acute
psychotic manifestations) is either true or false in the real world.
 Because the investigator cannot study all people who are at risk, he
must test the hypothesis in a sample of that target population.
 No matter how many data a researcher collects, he can never
absolutely prove (or disprove) his hypothesis.
 There will always be a need to draw inferences about phenomena in
the population from events observed in the sample (Hulley et al.,
2001).
 In some ways, the investigator’s problem is similar to that faced by a
judge judging a defendant
 The absolute truth whether the defendant committed the crime
cannot be determined.
 Instead, the judge begins by presuming innocence — the defendant
did not commit the crime.

36. Example Contd….
 The judge must decide whether there is sufficient evidence to
reject the presumed innocence of the defendant; the standard
is known as beyond a reasonable doubt.
 A judge can err, however, by convicting a defendant who is
innocent, or by failing to convict one who is actually guilty.
 In similar fashion, the investigator starts by presuming the null
hypothesis, or no association between the predictor and
outcome variables in the population.
 Based on the data collected in his sample, the investigator
uses statistical tests to determine whether there is sufficient
evidence to reject the null hypothesis in favor of the
alternative hypothesis that there is an association in the
population.
 The standard for these tests is shown as the level of
statistical significance.

37. Hypothesis testing
 Hypothesis testing is often referred to as
significance testing.
 A test of significance is conducted by
comparing the values of a statistics
computed from a sample with values
predicted by the sampling distribution under
the assumption that the null hypothesis is
true.
 Tests are made at essentially arbitrary
levels of significance, usually the 5 percent
or the 1 percent level.

38. Level of Significance
 For a difference to be taken as statistically
significant or not, the probability that the given
difference could have arisen ―by chance must be
ascertained.
 Before the investigator makes a judgment of
significance or non-significance, some critical
point(s) must be designated along the probability
scale which will serve to separate these two
judgment categories.
 For convenience, researchers have chosen
several arbitrary standards called level of
significance of which the 0.05 and 0.01 levels are
most often used.
 The confidence with which a researcher rejects or

39. Level of Significance
 Such level of significance must have been set before she
collects his/her data. It is not a good practice to shift from a
higher to a lower level after data have been collected. The
0.01 level of significance is more exacting than 0.05 level.
 The maximum probability at which we would be willing to risk
a type-1 error is known as the level of significance.
 In general 5% and 1% are taken as level of significance,
thereby indicating that on an average we may go wrong 5 out
of 100 cases and 1 out of 100 cases respectively.
 To say 5% level of significance, there is 95% confidence in
the result with a margin of error 5%.

40. Types of Errors
 Hypothesis testing involves risks because answers are
provided in terms of probability.
 Nobody is absolutely sure that the observed differences or
relationships between two variables are not due to chance.
 The probability value (p-value) is an indication of the odds
against the results of the study occurring by chance.
 There is the chance that the results obtained might have
been influenced by force other than the ones provided for in
the study.
 Therefore, the Null hypothesis may be rejected when it
should in reality be accepted.
 Alternatively, the Null hypothesis may not be rejected when
in reality it should have been rejected.

41. Types of Errors
 Type I Errors
 These errors are made when the researcher rejects a Null
hypothesis by making a difference or relationship significant,
although no true difference or relationship exists. In other words,
Type I error is committed by rejecting Null hypothesis when it is true,
thereby making a non-significant difference or relationship to appear
to be significant. The probability of rejecting a hypothesis(Ho) when
it is true. (also called level of significance/critical region) is Type I
error.
 Type II error: These errors are made when a researcher accepts a
Null hypothesis by making a difference or relationship not significant,
when a true difference or relationship actually exists. In other Words,
Type II error is committed by accepting Null hypothesis when it is not
true, thereby making a significant difference or relationship to appear
to be non-significant. The probability of accepting a hypothesis(Ho)
when it is false is Type –II error

42. Example of Type-I error
 For example, in a clinical trial of a new drug, the null
hypothesis might be that the new drug is no better, on
average, than the current drug; i.e. H0: there is no difference
between the two drugs on average. A type I error would occur
if we concluded that the two drugs produced different effects
when in fact there was no difference
 A type I error is often considered to be more serious, and
therefore more important to avoid, than a type II error.
 The hypothesis test procedure is therefore adjusted so that
there is a guaranteed 'low' probability of rejecting the null
hypothesis wrongly; this probability is never 0.
 This probability of a type I error can be precisely computed as
P(type I error) = significance level =

43. Example of Type-II errors
 For example, in a clinical trial of a new drug, the null
hypothesis might be that the new drug is no better, on
average, than the current drug; i.e. H0: there is no difference
between the two drugs on average.
 A type II error would occur if it was concluded that the two
drugs produced the same effect, i.e. there is no difference
between the two drugs on average, when in fact they
produced different ones.
 A type II error is frequently due to sample sizes being too
small.
 The probability of a type II error is generally unknown, but is
symbolised by and written P(type II error) =

44. Errors
Decision
Accept Ho Reject Ho
Ho (True) Correct
Decision
Type I error (α
error)
Ho (False) Type II error (β
error)
Correct
Decision

45. Ways to Reduce Type-I and II
errors
 Whenever the significance is doubtful or uncertain, the best
way to guide against both types of erroneous inference is to
demand or seek more evidence. Additional data, repetition of
the experiment and better controls will often make possible a
correct judgment.
 Setting a high level of significance tends to prevent Type I
errors but encourage the appearance of Type II errors. The
advise given is that the researcher must decide on which kind
of wrong inference he/she would rather avoid, as apparently
he/she can prevent one type of error only at the risk of
making the other more likely.
 The most generally acceptable practice is to set level of
significance of at least 0.0 1 in most experimental research,
that is, to risk Type II errors by preventing those of Type I.
However, it has been expressed that 0.05 level of significance
is often satisfactory, especially on preliminary work.

46. Power of Test
 The power of a statistical hypothesis test measures
the test's ability to reject the null hypothesis when it
is actually false - that is, to make a correct
decision.
 In other words, the power of a hypothesis test is
the probability of not committing a type II error. It is
calculated by subtracting the probability of a type II
error from 1, usually expressed as: Power = 1 -
P(type II error) =
 The maximum power a test can have is 1, the
minimum is 0.
 Ideally we want a test to have high power, close to
1.

47. Test Statistic
 A test statistic is a quantity calculated
from our sample of data. Its value is
used to decide whether or not the null
hypothesis should be rejected in our
hypothesis test.
 The choice of a test statistic will
depend on the assumed probability
model and the hypotheses under
question.

48. Critical Value(s)
 The critical value(s) for a hypothesis
test is a threshold to which the value
of the test statistic in a sample is
compared to determine whether or not
the null hypothesis is rejected.
 The critical value for any hypothesis
test depends on the significance level
at which the test is carried out, and
whether the test is one-sided or two-
sided.

49. Critical Region
 The critical region CR, or rejection region RR, is a
set of values of the test statistic for which the null
hypothesis is rejected in a hypothesis test.
 That is, the sample space for the test statistic is
partitioned into two regions; one region (the critical
region) will lead us to reject the null hypothesis H0,
the other will not.
 So, if the observed value of the test statistic is a
member of the critical region, we conclude "Reject
H0"; if it is not a member of the critical region then
we conclude "Do not reject H0".

50. Two tailed or one tailed test
 The critical region may be represented by a portion of the area
under the normal curve in two ways:-
 Two tailed test: The test of hypothesis which is used on critical
region represented by both the tails under the normal curve is called
two tailed test. A two tailed test applied in cases where it is
considered either a positive or negative difference between the
sample mean and the population mean is tending towards rejecting
of the null hypothesis.
 One tailed test: If the critical region is represented by only one tail
the test is called one tailed test. The one tailed test is applied in case
where it is considered that the population mean is at least as large
as some specified value of the mean or at least as small as some
specified value of the mean. In the former case right tail test is
applied and the latter case left tail test is applied.

51. Tests of significance:
Two tailed tests:
Non directional tests, or two-tailed tests,
are hypothesis tests where the
alternative hypothesis is stated as not
equal to (≠).
 Eg: H0: m = 558
Mean test scores are equal to 558 in the
population.
 H1: m ≠ 558
Mean test scores are not equal to 558 in
the population.

52.  For two-tailed tests, the alpha is split
in half and placed in each tail of a
standard normal distribution.
 This decides the rejection region.
Rejection areas
Fail to reject H0
Reject H0 Reject H0

53.  One tailed test:
 Directional tests, or one-tailed tests,
are hypothesis tests where the
alternative hypothesis is stated as
greater than (>) or less than (<) a
value stated in the null hypothesis .
 Eg: H0: m = 558
H1: m > 558

54.  For one-tailed tests, the alpha level is
placed in a single tail of a distribution.
 The rejection region will lie on either
side.
Rejection area Rejection area

55. Significance Level
 The significance level of a statistical hypothesis
test is a fixed probability of wrongly rejecting the
null hypothesis H0, if it is in fact true.
 It is the probability of a type I error and is set by the
investigator in relation to the consequences of such
an error. That is, we want to make the significance
level as small as possible in order to protect the
null hypothesis and to prevent, as far as possible,
the investigator from inadvertently making false
claims.
 The significance level is usually denoted by
Significance Level = P(type I error) = Alpha
 Usually, the significance level is chosen to be 0.05
(or equivalently, 5%).

56. p-value
 The probability value (p-value) of a statistical hypothesis test
is the probability of getting a value of the test statistic as
extreme as or more extreme than that observed by chance
alone, if the null hypothesis H0, is true.
 It is the probability of wrongly rejecting the null hypothesis if it
is in fact true.
 It is equal to the significance level of the test for which we
would only just reject the null hypothesis. The p-value is
compared with the actual significance level of our test and, if
it is smaller, the result is significant. That is, if the null
hypothesis were to be rejected at the 5% signficance level,
this would be reported as "p < 0.05".
 Small p-values suggest that the null hypothesis is unlikely to
be true. The smaller it is, the more convincing is the rejection
of the null hypothesis. It indicates the strength of evidence for
say, rejecting the null hypothesis H0, rather than simply
concluding "Reject H0' or "Do not reject H0".

57. Negative Findings
Even if hypotheses are not confirmed, they
have power.(Kerlinger, 1956)
Negative findings are as important as positive
ones, since they cut down ignorance and
sometimes point up fruitful hypotheses and
lines of investigation.
It acts as a guiding factor for future research
in that field.
Hypothesis cannot be proved or disproved;
but only supported or not supported.