What is a Hypothesis?
In Marketing Theory, a hypothesis is an unproven supposition or proposition that tentatively explains certain facts or phenomena.
Eg An Advertising Manager may hypothesise that consumers’ attitude towards the product will change positively if the packaging size is increased and hence sales will increase.
Eg. A Marketing Manger may hypothesise that
the sales of Brand A has decreased because of consumer switching to a competitive substitute product X
STEP 1 in Hypothesis
Testing is setting
The Null and Alternative
Hypothesis….
Hypothesis Testing
The purpose of hypothesis testing is to determine which of the two hypothesis is correct
The Null Hypothesis - Ho
A tentative assumption made about the value of a population parameter.
Usually it is a statement that the population parameter has a specific value
Hypothesis Testing
The Alternate Hypothesis - H1
A statement that will be accepted if our sample data provide us with ample evidence that the null hypothesis is false.
The alternate hypothesis describes what you will believe if you reject the null hypothesis.
STEP 2
in Hypothesis Testing is setting
The Level of Significance….
It is the probability of rejecting the null hypothesis when actually it is true
STEP 2
The Level of Significance….
This is the risk we assume of rejecting the null hypothesis when it is actually true.
Level of Significance or Level of Risk (alpha)
STEP 2
The Level of Significance….
There is no one level of significance
, that is applied to all studies involving sampling
A decision must be made to use the 0.5 level (5%), the 0.01 level, the 0.1 level, or any other level between
0 and 1.
STEP 2
The Level of Significance….
Traditionally the 0.5 level is selected for consumer research projects,
- for quality assurance and 0.1 level, for political polling.
Committing Type I and Type II Errors
Type I error - occurs when we reject the null hypothesis when it is actually true.
Type II error - occurs when we accept the null hypothesis when it is actually false.
Eg. Committing Type I Error
Eg. A sample of 100 people are being asked to taste a new product, which will only be launched if more than 10 per cent like the product and would consider buying it.
Ho: Accept if more than 10% like product
Imagine that 20% of the sample did not like the product. Ho is rejected, even though these were the only (20%) people from the whole target audience who in actual fact disliked the product (Type I error)
Step 1 State null & alternate hypotheses
Step 1 Select a level of Significance
Step 3 Identify the test statistic
(the value, determined from the sample
info., used to accept or reject Ho eg.
- Z (for large samples)
- t (for small samples 30 observations)
- F Distribution for (Analysis of Variance
(two population variances - eg to find out
whether two population means differ)
- F Distribution for (comparison of several -
three or more population means - ANOVA -
whether these produce different results
- Chi-Square 2 (which is used for frequency
distributions
The respective formula for the test statistic is applied.
Step 4 Formulate a Decision Rule
This is a statement of the conditions under which the null hypothesis is rejected and the conditions under which it is not rejected (the Area or Region of rejection is specified)
A two-tailed test is applied if no direction is specified
under the alternate hypothesis.
Some examples;
H0: There is no difference between the mean
sales of males and mean sales of females
H1: There is a difference in the mean sales of
males and the mean sales of females
(Two-tailed Test)
H0: There is no difference between the mean
sales of males and mean sales of females
H1: The mean sales of males is greater than the
mean sales of females.
(One-tailed Test)
Step 5 Take a Sample, arrive at decision to Accept the Null Hypothesis or reject the null hypothesis and accept the alternate hypothesis.
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