Business research presentation,Hypothesis

1. Hypothesis
-Hareesh M
2014600120

2. What is Hypothesis…?
• Hypothesis is considered as the principal
instrument in research.
• Its main function is to suggest new experiments
and observations
Definition:
• 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)

3. Hypothesis
• Hypothesis needs to be structured before the
data-gathering and interpretation phase of the
research
• The hypothesis gives direction to the
collection and interpretation of data

4. Characteristics of hypothesis
• Hypothesis should be clear and precise.
• Hypothesis should be capable of being tested
• Hypothesis should be limited in scope and
must be specific
• Hypothesis should state relationship between
variables
• Hypothesis should be consistent with most
known facts, amenable to testing within a
reasonable time

5. Null and Alternative Hypothesis
• The Null hypothesis (H0) is a claim of “no
Significance difference between 2 variables”
• The Alternative hypothesis (Ha) claims “H0 is
false”. Opp to Null Hypothesis

6. Type-1 and type-2 errors
Type I error:
•Rejection of a true null hypothesis is called the type I error.
•Type error I occurs when we reject a hypothesis that should
be accepted.
Type II error:
•Acceptance of false null hypothesis is called the type II error.
•Type error II occurs when we accept a hypothesis that should
be
rejected.

7. Confidence interval
• It is expressed as a percentage and represents
how often the true percentage of the population
who would pick an answer lies within the
confidence interval.
• The 95% confidence level means you can be 95%
certain;
• the 99% confidence level means you can be 99%
certain.
• Most researchers use the 95% confidence level.

8. Level of Confidence
• A confidence level refers to the percentage of
all possible samples that can be expected to
include the true population parameter. For
example, suppose all possible samples were
selected from the same population, and a
confidence interval were computed for each
sample. A 95% confidence level implies that
95% of the confidence intervals would include
the true population parameter.

9. Hypothesis testing of means
Mean of the population can be tested presuming
different situations such as the population may be
Normal or other than normal, its sample size may be
large or small, variance of the population may be
known or unknown and the alternative hypothesis may
be two-sided or one sided. our testing technique will
differ in different situations.
We may consider some of the important
Situations.

10. Hypothesis testing of means

11. Hypothesis testing of means

12. Hypothesis testing for differences
between means
The null hypothesis for testing of difference between means
is generally stated as H0 : m1 = m2 ,
• where m1 is population mean of one population and m2 is
population mean of the second population, assuming both
the populations to be normal populations.
• Alternative hypothesis may be of not equal to or less than
or greater than type as stated earlier and accordingly we
shall determine the acceptance or rejection regions for
testing the hypotheses.
• There may be different situations when we are examining
the significance of difference between two means, but the
following may be taken as the usual situations:

13. Hypothesis testing for differences
between means

14. Hypothesis testing for differences
between means

15. Hypothesis testing for differences
between means