THIS POWERPOINT EXPLAINS ABOUT HYPOTHESIS AND ITS TYPES, ROLE OF HYPOTHESIS,TEST OF SIGNIFICANCE AND PROCEDURE FOR TESTING A HYPOTHESIS, TYPE I AND TYPE ii ERROR

2. Hypothesis
 Hypothesis means a mere assumption or
some supposition or a possibility to be
proved or disproved
 For a researcher, hypothesis is a formal
questions to be resolved.

3.  A hypothesis is a tentative generalization,
the validly of which remains to be tested –
Geroge A.Lundberg
 A proposition which can be put to test to
determine validity -Goode and Hatt
 A hypothesis is a statement capable of being
tested and thereby verified or rejected-
Rummel and Balline

4. Hypotheses reflect the research worker’s guess
as to the probable outcome of his experiment
and they place clear and specific goals before
the researcher and provide him with a basis for
selecting samples and research procedures to
meet these goals
-Walter R.Borg
A hypothesis is an atempt at explanation: a
provisional supposition made in order to
explain scientifically some facts or phenomenon
-Coffey
-

5. Different types of hypothesis
 Descriptive hypothesis
 Descriptive hypotheses are prepositions that describe
the existence, size, form or distribution of some
variables
Eg: The per capital income of Indian is lower than that of
Chinese
Relational Hypothesis
It describes the relationship between two
variables.
Eg: “Families with higher income spend
more on recreation”

6.  Working hypothesis
The working hypothesis indicates the nature of data and
methods of analysis for the study. Working hypothesis are
subject to modification as the investigation proceeds.
 Null hypothesis:
when a hypothesis is stated negatively, it is called a null
hypothesis. A null hypothesis should always be specific. The
null hypothesis is the one which one whishes to disprove
- It is generally a symbolized as H0
eg: “Age of the respondents does not influence their job
satisfaction:
“The drug is not effective in curing malaria”

7.  Alternative hypothesis
 The set of alternatives to the null hypothesis is referred to as
the alternative hypothesis. Alternative hypothesis is usually the
one which one wishes to prove.
-It is generally a symbolized as Ha
Eg:“Age of the respondents influences their job satisfaction”
 Statistical hypothesis:
it is a quantitative statement about a population. When the
researcher derives hypothesis from a sample and hopes it to
be true for the entire population it is known as statistical
hypohtesis.
eg: “Group X is older than Group Y”

8.  Simple hypothesis or Common sense hypothesis:
-- It states the existence of certain empirical uniformities.
Many empirical uniformities are common in sociological
research.
eg: Fresh students conform to the conventions set by the
seniors.
 Composite hypothesis:
These hypothesis aim at testing the existence of logically
derived relationship between empirical uniformities obtain

9.  Explanatory hypothesis
 It states the existence of one independent variable
causes or leads to an effect on dependent variable
 Eg: “yield of tomato is influenced by the application of
fertilizers”

10. Role of a hypothesis
1. A hypothesis gives a definite way to the investigation and
it guides the direction of the study
2. It systematizes knowledge and helps in the verification of
facts.
3. It helps in that test of theories developed by the social
scientist to explain a phenomenon
4. It determines the most appropriate technique of analysis
5. It describes the relationship between two variables
6. It helps in drawing a specific conclusion

11. What is hypothesis testing?
 Hypothesis testing refers to
1. Making an assumption called hypothesis about a
population parameter
2. Collecting sample data.
3. Calculating a sample statistic.
4. Using the sample statistic to evaluate the hypothesis (how
likely is it that our hypothesized parameter is correct. To
test the validity of our assumption we determine the
difference between the hypothesized parameter value and
the sample value.)

12. Hypothesis testing
 It means to test some hypothesis about parent population
from which the sample is drawn.
For example: we can take a hypothesis that sugarcane
cultivated in Cubcum valley has an average height of 200cms.
Estimation:
It means to use the statistics obtained from the sample as
estimate of the unknown parameter of the population.
For example, a researcher may like to know about the average
weight and variance in number of young ones yielded by a
rabbit during a given period on the basis of one or more
young ones drawn from the numbers.
In both cases , the inference about the population values made
from sample data

13. Test of significance
A procedure to assess the
significance of a statistics or
difference between two
independent statistics is known
as test of significance

14. Procedure for testing a hypothesis
1. Set up hypothesis
hypothesis is a tentative conclusion logically drawn
about a population parameter.
First step is to set up a hypothesis of assume a certain value for
a population mean
For example:
 The average weight of fish reared in a research station is
2.6kg
 A given antibiotic cures 80% of the patients taking it.
 The cost of production of wheat is less than any other
eatable cultivation

15.  To test the validity of our assumption
(hypothesis), we gather sample data and
determine difference between hypothesized value
and the actual value of the sample mean.
 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.
Thus, if the statistical tests show that the difference
is significant, the hypothesis is rejected.

16. Two hypothesis
Null hypothesis
Alternative hypothesis
Null hypothesis:
 This hypothesis asserts that there is no true
difference in the sample and the population in the
particular matter under consideration and that the
difference is accidental and unimportant arising
out of fluctuations of sampling.
 In other words, a hypothesis which is stated
for the purpose of possible acceptance is
called null hypothesis

17.  Null hypothesis id usually denoted by H0.
If we want to find out whether a particular
drug is effective in curing malaria, we will take the null
hypothesis that “the drug is not effective in curing
malaria”.
The rejection of the null hypothesis indicates that the drug
is effective in curing malaria ie. It indicates success in the
project.

18.  The null hypothesis may be used under the following
conditions:
1. If we want to test the significance of the difference
between a sample statistics and population parameter r
between sample statistics, we set null hypothesis that the
difference is not true
H0:µ= mean
2. To test any statement about the population, we set up the
null hypothesis that it is true. For example, if we want to
find out whether average yield of paddy per hectare in
Tamil Nadu is 10 tones, then we set up null hypothesis.
H0:µ= 10 tonnes

19. Alternative hypothesis
 Any hypothesis which is complementary to the null
hypothesis is called alternative hypothesis and is denoted
as H1 or Ha
 For example, if we want to test the null hypothesis that
the average height of the soldiers is 162cm
H0=µ=162cm
Alternative hypothesis could be
H1 :µ =162cm
H1:µ >162cm
H1:µ <162cm

20. Set up a suitable significance level (or)
Selecting and interpreting significance level
1. Deciding on a criterion for accepting or rejecting the
null hypothesis.
2. Significance level refers to the percentage of sample
means that is outside certain prescribed limits. E.g
testing a hypothesis at 5% level of significance means
that we reject the null hypothesis if it falls in the two
regions of area 0.025.
 Do not reject the null hypothesis if it falls within the
region of area 0.95.
3. The higher the level of significance, the higher is the
probability of rejecting the null hypothesis when it is
true. (acceptance region narrows)

21. Type I and Type II Errors
 1.Type I error refers to the situation when we reject the null
hypothesis when it is true (H0 is wrongly rejected).
e.g H0: there is no difference between the two drugs on average.
Type I error will occur if we conclude that the two drugs produce
different effects when actually there isn’t a difference.
Prob(Type I error) = significance level = α
2. Type II error refers to the situation when we accept the null
hypothesis when it is false.
H0: there is no difference between the two drugs on average.
Type II error will occur if we conclude that the two drugs produce
the same effect when actually there is a difference.
Prob(Type II error) = ß

22. Select test criterion
 This involves selecting an appropriate probability
distribution for the particular test.
 Some common probability distribution which are used
in testing are Z, t, chi square or F distributions. While
testing, appropriate probability distribution should be
applied

23. Doing computations
 These calculations include the testing statistics and
standard error of the testing statistics

24. Making decisions
 The final step is to decide whether to accept the
hypothesis or reject the null hypothesis
 the decision depends on whether the computed value
of the test criterion falls in the region of rejection or in
the region of acceptance.
 that is if the calculated value of test criterion is greater
than table value, then we can reject the null
hypothesis. If it is less than, we can accept hypothesis

25. Reference
 Statistical methods for Biologists_S.Palanichamy and
M.Manoharan
 Research methodology – Dr.Peer mohamed, Dr.Akbar
batcha and Dr. Shazuli ibrahim