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  3. A/B Testing in Python

Exercise

SRM

When we design an experiment to allocate enrollment units (e.g. users) at a given percentage per variant, we expect some slight variations to happen due to logging issues, delays, minor instrumentation bugs, etc. When that deviation is larger than what is expected, however, this is usually an indication of a larger issue that could invalidate and bias our test results. The goal of this exercise is to examine the statistical techniques that enable you to catch cases where the allocation mismatch is too large to be blamed on chance alone.

As an analytics engineer, your role may require you to design and even automate frameworks for catching sample ratio mismatches in A/B tests. The checkout DataFrame is loaded for you along with pandas and numpy libraries. Let's consider the control group to be checkout design 'A' and the treatment group to be 'B'.

Instructions 1/3

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  • Calculate the number of unique users (user_id) in groups 'A' and 'B' and assign the total number of users to the variable total_users.