Stat

1. DR. NALINI R
ASSOCIATE PROFESSOR OF COMMERCE
DEPARTMENT OF MBA
MAHARANI’S WOMEN’S COMMERCE AND
MANAGEMENT COLLEGE, MYSURU

2. HYPOTHESES : CONTENTS
 FORMULATION OF HYPOTHESES
 HYPOTHESIS : NECESSARY OR NOT?
 TYPES
 SOURCES
 FUNCTIONS
 CHARACTERISTICS
 TESTING OF HYPTHESIS
 TYPES OF ERROR

3. Formulation of Hypotheses
 Meaning of Hypothesis: A tentative proposition
formulated for empirical testing
 It’s a tentative answer to a research question
 Good & Hatt: “ a proposition which can be put
to a test to determine its validity”

4. Hypotheses: Necessary or Not?
 In mere fact finding investigation: no need
 In exploratory studies: no need as it is not
possible & the very purpose is to help in
formulating hypotheses
 In analytical & experimental studies: must be
set up to give clear direction to the study

5. Types of Hypotheses
 Null Hypotheses
 Alternative Hypotheses

6.  Null Hypothesis: These are hypothetical statements
denying what are explicitly indicated in working hypo.
They do not exist in reality; nor intended to exist in
reality
 They state that no difference exists between the
parameter and the statistic being compared to it
 Ex: “ There is no relationship between families’ income
level and expenditure on recreation”
 Null hypotheses are formulated for testing statistical
significance
 As the test would nullify the null hypo they are called
so (utility of Null hypo: Objectivity)

7. Sources of Hypotheses
 Theory : Goal of business (theory) Hypo: the rate of
return on CE is an index of business success; higher
the EPS more favorable is the financial leverage
 Observation: Ex: price & demand for a product
 Intuition & Personal experience
 Findings of Studies
 Continuity of research

8. Functions or Role of Hypotheses
 Guides the direction of study
 Gives an idea for setting order among facts
 Specifies sources of data
 Determines data needs
 Suggests type of research
 Determines the technique of analysis
 Helps in development of theories

9. Characteristics of a good hypothesis
 Conceptually clear
 Specificity
 Testability
 Availability of techniques
 Theoretical relevance
 Consistency
 Objectivity
 Simplicity

10. Testing of Hypotheses
 Embodies major part of research process
 Consists of operationalisation of concepts,
construction of data gathering tools, collection of data
, statistical analysis of data and drawing inferences
from the results.
 Tests of significance are applied
 Facts may confirm the hypothesis or reject

11. Types of Error in testing of hypothesis
 We may reject a hypothesis which is true and
should not be rejected (Type I Error);
 We may accept a hypothesis which is false and
should be rejected (Type II Error)

12. Null Hypothesis
 Develop a Null Hypo for statistically testing the hypo
 The H0 is formulated for testing possible rejection or
nullification
 It is accompanied by alternative hypo Ha
 Our regular research hypo is the logical opposite of
null hypo
 Ex: Implementation of Employment scheme has not
led to an increase in the average rural income level
 Null hypo always states that there “ no change” or “ no
difference” or “no relationship”

13. Hypothesis testing
 Both the null hypo and the research hypo are
expressed in terms of the population parameters not
in terms of the sample statistics
 Null hypo is tested directly; the research hypo is
supported when the null hypo is rejected
 As a sample is likely to vary somewhat from its
population, the sample result is subject to sampling
error & so it does not always reflect true population
value.
 So sample results cannot be interpreted directly; a
decision rule is needed to enable us to accept or reject
a hypothesis on the basis of sample result

14. When does the sample result justify the rejection of null
hypothesis?
 When it could not be a result of chance (when it is highly
improbable)
 Statisticians consider only when if it is among the extreme
5% or 1% of possible outcomes
 The 5% or 1% value defines the proportion of a sampling
distribution that contains the specified highly improbable
outcomes
 This proportion of the distribution is called the rejection
region and the 5% or 1% are called levels of significance or
levels of confidence. These are levels of probability at
which the null hypo can be rejected with confidence.
 The rejection of H0 supports the research hypo