Most popular statistical tests are based on the assumption that your data comes from a normal distribution. These tests are unreliable when that assumption is wrong. This is a massive problem with Excel’s native testing capabilities, because Excel does not have a way to test for normality, not even in their Analysis Toolpak (which I discuss in-depth in this 3-part series).

If you try and find information about how to conduct a normality test in Excel, this is what you’ll mostly find:

• Guides that tell you how to create a normal probability plot. While a probability plot can be a nice visual aid, your conclusion is completely subjective. Two people can look at the exact same plot, and arrive at different conclusions about normality.
• Videos or guides that are really just advertisements for an Excel plugin you will have to pay for.
• Lengthy guides explaining how to set up a normality test in Excel which are not very self-explanatory, and that are usually based on the chi-square goodness of fit test, simply because it’s the easiest test to set up in Excel. Unfortunately, this doesn’t mean it’s the best test, and it still takes a lot of time to set up.

Rather than put you through all that, I decided I would just put up a spreadsheet that you can use right away.

This spreadsheet is based on the Shapiro-Wilk test, which is the most powerful of the popular normality tests according to a paper (PDF link) published in the Journal of Statistical Modeling and Analysis.

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