How Use Excel T.TEST – Was a Marketing Campaign Successful?
Like z-tests, t-tests are calculations used to test a hypothesis, but they are most useful when we need to determine if there is a statistically significant difference between two independent sample groups. In this article we will look at T.TEST in Excel.
A t-test asks whether a difference between the means of two groups is unlikely to have occurred because of random chance. Usually, t-tests are most appropriate when dealing with problems with a limited sample size (n < 30).
Now you might think that comparing the averages are easy, but averages are affected by outliers and can give misleading results. You may also have situations where the sample and population sizes are different. Lets say you have a drug for the flu, and patients that take it get better on average in 3 days, and patients that don’t get better on average 5 days. The T. Test can be used to see if this difference if by fluke or because of the drug.
Another Example would be to test to see if the change in sales are down to a successful marketing campaign or if the change in sales happened by chance.
Example
Our table of data represents the age of employees within a company. There are 6 male employees with an average age of 33 and 5 female employees with an average age of 24.8.
We wish to see if the difference between the mean of the males age and the mean of the female age is unlikely or likely to have happened by chance.
We can write out our hypothesis test as:
- HO the difference between the mean of the males age and the mean of the female age is unlikely to have happened by chance and have no statistically significant difference
- HA the difference between the mean of the males age and the mean of the female age is likely to have happened by chance and have a statistically significant difference
Understanding T.TEST requirements
Lets first look at the syntax of the T.TEST
=T.TEST(array1, array 2, tails, type)
Where array 1 and 2 are the observations.
Tails; select between 1 tailed or 2 tailed. A one tailed test is where only an increase or decrease in observations between tests. Two tailed tests are where you would find both increases and decreases in observations between tests. Looking at our data, both the male observations ages increase and decrease and so does the female observations. Therefore, we have a 1 tailed test.
The last item in T.Test is Type. We must select between paired, two sample equal variance and two sample unequal variance.
A paired sample is where the same sample is used for both tests. If the test is not a paired you must then select between equal or unequal variances.
F.TEST in Excel
To test your sample data and see if you have equal or unequal variances you can use excels F.TEST function. The F-Test is used to test the null hypothesis that the variances of two populations are equal, the alternative hypotheses is that the populations have unequal variances.
We could write out our hypothesis as
- HO variances of two populations are equal
- HA variances of two populations are not equal
The syntax for F.TEST is
=F.TEST(array1, array2)
Where array 1 and 2 are the observations.
The result will be a value of <=1. 1 representing a totally equal variance, the smaller the number gets the larger the variances. The result is the probability shown as a two tailed test. If you have a 1 tailed test then the values should be divided by 2. In this case we have already established this is a one tailed test.
If the value returned is >0.05 (alpha) we would accept the null hypothesis that the variances of two populations are equal. If the value is not equal to <=0.05 we would reject the null hypothesis. As the value for the one tailed test is greater than 0.05 we can therefore accept the null. As we know now that the variances are equal we can go ahead with our T.TEST
Compiling the T.TEST
=T.TEST(array1, array 2, tails, type)
Array 1 being male employees, array 2 being female. We know we have a 1 tailed test and we know they type is of equal variance.
The T.TEST returns a P value. This P value is the probability that there is no statistically significant difference between the two data sets.
If the P value returned is >0.05 (alpha) we would accept the null hypothesis that the variances of two populations are equal. If the value is not equal to <=0.05 we would reject the null hypothesis.
As the P value returned in this case is 0.102, greater than 0.05, we can accept the null hypothesis and say there is a statistically significant differences between the age of male employees and female employees which did not happen by chance.
Here is an example for you to practice.
A company runs a marketing campaign and has sale values for the 7 days before and for the 7 days of the campaign. They wish to establish if the increase in average sales was down to the marketing campaign or just by fluke.
We wish to see if the difference between the mean sales pre-campaign and and the mean of the during the campaign is likely or unlikely to have happened by chance.
Copy the table of data below and work out if you could call the marketing campaign a success or not by carrying out a T.TEST
Pre Marketing | During Marketing | |
Monday | 649 | 1070 |
Tuesday | 654 | 799 |
Wednesday | 961 | 575 |
Thursday | 816 | 940 |
Friday | 663 | 917 |
Saturday | 623 | 714 |
Sunday | 599 | 748 |
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