t tests - excel

T‐Tests Microsoft Excel 2010

Applied Statistics Laboratory

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This tutorial explains how to perform one sample, two sample, and paired t‐tests in

Excel.

One Sample T‐test

One sample t‐tests test whether the population mean underlying a sample set of

numbers is different from a selected value. Excel does not have a one‐sample t‐test

function. To work around this, replicate the number you are testing against (your

selected value) as a second group. In the following example, we wish to test whether

the “Sample” is different from the historical control of 8.

Step 1: Click and highlight the empty cell where you would like results of the t‐test to

be placed.



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Step 2: Click the “fx” at the top of the spreadsheet. Once selected, the following screen

will appear.



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Step 3: Select the statistical category.



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Step 4: Scroll down, select the TTEST (also seen as T.TEST) function and click OK.

The following dialog box will appear.



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Step 5: For Array 1, highlight first set of data. After you have highlighted, Excel will

automatically fill in that argument.



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Step 6: Repeat for Array 2, highlighting the second group of data.



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Step 7: Tails specifies the number of distribution tails to return; type “2” for hypothesis

of difference (two‐sided test).

Use “3” as the t‐test type.



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Step 8: Click OK. The result of this function is the p‐value for your t‐test.

A p‐value below or equal to your significance level (typically 0.05) indicates a

statistically significant difference; a p‐value greater than your significance level

indicates no statistical difference.

In this example, 0.004 is less than the significance level of 0.05. Therefore, we conclude

that the sample is significantly different from the historical control of 8.



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Two Sample T‐test

Two sample t‐tests test whether the population means underlying two sample sets of

numbers are different from each other. There are two forms of the two sample t‐tests.

The first is the one‐sided test, which is appropriate when we wish to learn whether the

mean of one group is greater than the mean of the other group. The second is the two‐

sided test, which is appropriate when we wish to learn merely whether the means of the

two groups are different. In the following example, we wish to utilize the two‐sided test

to determine whether the mean for “Group A” is different from the mean for “Group

B”.


be placed.



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will appear.



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Step 7: Tails specifies the number of distribution tails to return; type “1” for the one‐

sided test, type “2” for the two‐sided test.

Use Type “2” as the t‐test type if assuming equal variance; use Type “3” if assuming

unequal variance.



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Step 8: Click OK. This function gives the p‐value for your t‐test.




In this example, 0.192 is greater than the significance level of 0.05. Therefore, we

conclude that there is not a statistical difference between Group A and Group B.

Alternatively, we could state that there is insufficient evidence to determine whether

there is a difference in Group A and Group B.



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Paired T‐test

Paired t‐tests test whether two set of related numbers are different from one another.

There are two forms of the paired t‐tests. The first is the one‐sided test, which is

appropriate when we wish to learn whether the mean of one group is greater than the

mean of the other group. The second is the two‐sided test, which is appropriate when

we wish to learn merely whether the means of the two groups are different. In the

following example, we wish to utilize the two‐sided test to determine whether the mean

for “Pre” is different from the mean for “Post”.


be placed.



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will appear.



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Step 7: Tails specifies the number of distribution tails to return; type “1” for the one‐

sided test, type “2” for the two‐sided test.

Use “1” as the t‐test type.



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Step 8: Click OK. This function gives the p‐value for your t‐test.




In this example, 0.196 is greater than the significance level of 0.05. Therefore, we

conclude that there is not a statistical difference between Pre and Post. Alternatively, we

could state that there is insufficient evidence to determine whether there is a difference

in Pre and Post.

t tests - excel

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