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Many statistical tests make the assumption that the values in a dataset are normally distributed.

One of the easiest ways to test this assumption is to perform aÂ **Jarque-Bera test**, which is a goodness-of-fit test that determines whether or not sample data have skewness and kurtosis that matches a normal distribution.

This test uses the following hypotheses:

H_{0}: The data is normally distributed.

H_{A}: The data isÂ *not* normally distributed.

The test statisticÂ * JBÂ *is defined as:

* JBÂ * =(n/6) * (S

^{2}+ (C

^{2}/4))

where:

**n:**Â the number of observations in the sample**S:Â**the sample skewness**C:**the sample kurtosis

Under the null hypothesis of normality,Â *JB ~Â *X^{2}(2).

If the p-value that corresponds to the test statistic is less than some significance level (e.g. Î± = .05), then we can reject the null hypothesis and conclude that the data is not normally distributed.

This tutorial provides a step-by-step example of how to perform a Jarque-Bera test for a given dataset in Excel.

**Step 1: Create the Data**

First, letâ€™s create a fake dataset with 15 values:

**Step 2: Calculate the Test Statistic**

Next, calculate the JB test statistic. Column E shows the formulas used:

The test statistic turns out to beÂ **1.0175**.

**Step 3: Calculate the P-Value**

Under the null hypothesis of normality, the test statistic JB follows a Chi-Square distribution with 2 degrees of freedom.

So, to find the p-value for the test we will use the following function in Excel: **=CHISQ.DIST.RT(JB test statistic, 2)**

The p-value of the test is **0.601244**. Since this p-value is not less than 0.05, we fail to reject the null hypothesis. We donâ€™t have sufficient evidence to say that the dataset is not normally distributed.

In other words, we can assume that the data is normally distributed.

**Additional Resources**

How to Create a Q-Q Plot in Excel

How to Perform a Chi-Square Goodness of Fit Test in Excel