1. Learn
    2. /
  2. Courses
    3. /
  3. A/B Testing in Python

Exercise

Novelty effects detection

Novelty effects happen more often than most Data Scientists and Engineers would expect. Running an A/B test on one new cool feature and calling the decision after seeing a big uptick in usage metrics over the first few days is one of the common mistakes that junior analysts fall into.

The novelty dataset that is loaded for you, has data about the difference in average time on page per user (ToP) between two variants. Examine the results over time and check if there are any signs of novelty effects. Would you include all the results form the start to the end of the test?

Instructions 1/2

undefined XP
    1
    2
  • Plot the lift in the Time-on-Page metric ToP_lift over the experiment's duration.