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# How to Perform a Normality Test in Excel (Step-by-Step)

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:

H0: The data is normally distributed.

HA: The data isÂ not normally distributed.

The test statisticÂ JBÂ is defined as:

JBÂ  =(n/6) * (S2 + (C2/4))

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 X2(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.