Shapiro-Wilk Test Python
import numpy as np from scipy.stats import shapiro import seaborn as sns alpha = 0.05 np.random.seed(11) #Uniform Example General
rolls = np.random.randint(1, 7, size=30) sns.histplot(rolls) 
stat, shapiro_p_value = shapiro(rolls) print(shapiro_p_value) 
if shapiro_p_value > alpha: print("The data is likely normally distributed (fail to reject H0).") else: print("The data is NOT normally distributed (reject H0).") 
#Example with a Paired T Test
ticket_sales_before = np.array([240000, 270000, 255000, 264000, 258000, 252000, 246000, 243000]) ticket_sales_after = np.array([540000, 600000, 585000, 630000, 615000, 660000, 645000, 690000]) ticket_sales_diff = ticket_sales_after - ticket_sales_before stat, shapiro_p_value = shapiro(ticket_sales_diff) if shapiro_p_value > alpha: print("The data is likely normally distributed (fail to reject H0).") else: print("The data is NOT normally distributed (reject H0).") 
