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).")
