Exercise

# Statistical tests for normality

In order to truly be confident in your judgement of the normality of the stock's return distribution, you will want to use a true statistical test rather than simply examining the kurtosis or skewness.

You can use the `shapiro()`

function from `scipy.stats`

to run a Shapiro-Wilk test of normality on the stock returns. The function will return two values in a list. The first value is the t-stat of the test, and the second value is the p-value. You can use the p-value to make a judgement about the normality of the data. If the p-value is **less than or equal to 0.05**, you can safely reject the null hypothesis of normality and assume that the data are non-normally distributed.

`clean_returns`

from the previous exercise is available in your workspace.

Instructions

**100 XP**

- Import
`shapiro`

from`scipy.stats`

. - Run the Shapiro-Wilk test on
`clean_returns`

. - Extract the p-value from the
`shapiro_results`

tuple.