01-05-2021



Certification

This example teaches you how to perform a t-Test in Excel. The t-Test is used to test the null hypothesis that the means of two populations are equal.

Learn how to use Excel in exactly 30 minutes starting with the very basics (for the complete beginner) and then move to more intermediate features such as ch. Open Microsoft Excel. You'll find it in the Start menu (Windows) or in the Applications folder (macOS). The app will open to a screen that allows you to create or select a document. If you don't have a paid version of Microsoft Office, you can use the free online version at to create a basic spreadsheet. This example teaches you how to perform a t-Test in Excel. The t-Test is used to test the null hypothesis that the means of two populations are equal. Below you can find the study hours of 6 female students and 5 male students. H 0: μ 1 - μ 2 = 0 H 1: μ 1 - μ 2 ≠ 0.

Below you can find the study hours of 6 female students and 5 male students.

H0: μ1 - μ2 = 0
H1: μ1 - μ2 ≠ 0

To perform a t-Test, execute the following steps.

1. First, perform an F-Test to determine if the variances of the two populations are equal. This is not the case.

2. On the Data tab, in the Analysis group, click Data Analysis.

Note: can't find the Data Analysis button? Click here to load the Analysis ToolPak add-in.

3. Select t-Test: Two-Sample Assuming Unequal Variances and click OK.

4. Click in the Variable 1 Range box and select the range A2:A7.

5. Click in the Variable 2 Range box and select the range B2:B6.

6. Click in the Hypothesized Mean Difference box and type 0 (H0: μ1 - μ2 = 0).

7. Click in the Output Range box and select cell E1.

Me Excellence

8. Click OK.

Result:

Conclusion: We do a two-tail test (inequality). lf t Stat < -t Critical two-tail or t Stat > t Critical two-tail, we reject the null hypothesis. This is not the case, -2.365 < 1.473 < 2.365. Therefore, we do not reject the null hypothesis. The observed difference between the sample means (33 - 24.8) is not convincing enough to say that the average number of study hours between female and male students differ significantly.

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