Hypothesis Testing is a kind of data testing used in marketing research. It helps you to test the give data to gain proper insights about it. Check errors if there and rectify it.

1. HYPOTHESIS
TESTING

2. WHAT IS
HYPOTHESIS
TESTING?
Making assumption about
population parameter
Collecting sample data
Calculating a sample statistic
an analyst tests an assumption
regarding a population
parameter.
Hypothesis testing refers to:

3. Hypothesis Testing
Null Hypothesis(H0) Alternative Hypothesis(H1)
Hypothesis that states that
there is no relationship
between two population
parameters.
Researchers reject or
disprove the null hypothesis
to set the stage for further
experimentation or research
that explains the position of
interest.
It is a statement used in
statistical inference
experiment.
We can also say that it is simply
an alternative to the null.
In hypothesis testing, an
alternative theory is a
statement which a researcher is
testing.

4. A type of conjecture
in statistics that
proposes that there is
no difference
between certain
characteristics of a
population or data-
generating process.
NULL HYPOTHESIS (H0)
A null hypothesis is a
type of statistical
hypothesis that
proposes that no
statistical significance
exists in a set of given
observations.
It is used to assess
the credibility of a
hypothesis by using
sample data.

5. We can also say
that it is simply
an alternative to
the null.
In hypothesis
testing, an
alternative
theory is a
statement which
a researcher is
testing.
ALTERNATING HYPOTHESIS
The alternative
hypothesis is a
statement used in
statistical
inference
experiment.
It is contradictory
to the null
hypothesis.

6. Null Hypothesis Alternative Hypothesis
It denotes there is no relationship
between two measured phenomena.
It’s a hypothesis that a random cause
may influence the observed data or
sample.
It is represented by H0 It is represented by Ha or H1
Example: Rohan will win at least Rs.100000
in lucky draw.
Example: Rohan will win less than
Rs.100000 in lucky draw.

7. STEP 1:
STEP 2:
STEP 3:
STEP 4:
STEP 5:
State your null and alternate Hypothesis
Collect data
Perform a statistical test
Decide whether to reject or fail to reject your
null hypothesis
Present your findings
Steps for
testing
hypothesis

8. Setup Hypothesis
Null Hypothesis and Alternative
Hypothesis
PROCEDURES
FOR TESTING
HYPOTHESIS Set up a suitable significance level
Setting a test criterion
t-test, F and chi square (χ 2 )
Doing Computation
Making Decisions

9. TWO TYPES
OF ERRORS
IN TESTING
OF
HYPOTHESIS
The hypothesis is true but our
test rejects it.
Denoted by α.
The hypothesis is false but our
test accepts it.
Denoted by β.
Type I Error:
Type II Error:

10. TWO TAILED TEST OF HYPOTHESIS
In statistics, a two-tailed test is a method in which the critical area of a
distribution is two-sided and tests whether a sample is greater or less than a
range of values.
It is used in null-hypothesis testing and testing for statistical significance.
If the sample being tested falls into either of the critical areas, the
alternative hypothesis is accepted instead of the null hypothesis.
By convention two-tailed tests are used to determine significance at the 5%
level, meaning each side of the distribution is cut at 2.5%.

11. One tailed test of hypothesis
A one-tailed test is a statistical hypothesis test
set up to show that the sample mean would be
higher or lower than the population mean, but
not both.
When using a one-tailed test, the analyst is
testing for the possibility of the relationship in
one direction of interest and completely
disregarding the possibility of a relationship in
another direction.
Before running a one-tailed test, the analyst
must set up a null and alternative hypothesis
and establish a probability value (p-value).

12. THANK
YOU!
Have a
great day
ahead.