T-test for difference in means
You're a Data Scientist at an e-commerce company tasked with helping the payments team choose the checkout page design that leads to higher order value and faster purchase decisions.
After estimating the required sample size and passing all sanity checks, you'll analyze differences in the average order_value and time_on_page between checkout_page variants to decide which design performs best.
The checkout DataFrame is available, and pingouin, pandas, and numpy have been loaded for you.
NaN values in order_value may indicate users who didn't complete a purchase. You'll analyze the average order value only among completed orders (complete data).
Den här övningen är en del av kursen
A/B Testing in Python
Interaktiv övning med praktiskt arbete
Testa den här övningen genom att slutföra den här exempelkoden.
# Calculate the mean order values and run a t-test between variants A and B
print(checkout.____('checkout_page')['____'].____())
ttest = ____.____(x=checkout[checkout['checkout_page']=='____']['____'],
y=checkout[checkout['checkout_page']=='____']['____'],
paired=____,
alternative="____")
print(ttest)