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.

Import shapiro from scipy.stats.
Run the Shapiro-Wilk test on clean_returns.
Extract the p-value from the shapiro_results tuple.

Exercise

Statistical tests for normality

Instructions