Simpson's paradox in action
Generalizing our A/B test results to different segments of the population can be of utmost importance to the business. Sometimes we want to save the cost of running other tests in different cities, by different devices, etc. Making sure that our results are consistent by subpopulations can increase our confidence to make such generalizations.
Examine the simp_balanced and simp_imbalanced datasets for Simpson's paradox to gain a good sense for how this phenomena can occur in A/B testing scenarios.
Deze oefening maakt deel uit van de cursus
A/B Testing in Python
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Calculate the conversion rate per variant and then browser
imbalanced_variant_rate = simp_imbalanced.____('____')['____'].____()
imbalanced_variant_browser_rate = simp_imbalanced.____(['____','____'])['____'].____()
print(imbalanced_variant_rate)
print(imbalanced_variant_browser_rate)