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  3. A/B Testing in Python

Exercise

Distributions balance

Another way we can quickly check for randomization bias in our A/B tests is by looking at how balanced or imbalanced the distributions of metrics and attributes that shouldn't change between the different variants. Any major differences in the percentage of certain devices, browsers, or operating systems for examples, assuming our samples are large enough, could be symptoms of larger problems in our internal setup. Examine the AdSmart and checkout datasets that are loaded for you and check for internal validity using the attributes distributions. Which dataset seems to have a more valid internal setup?

The Adsmart Kaggle dataset source is linked here.

Instructions 1/2

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  • 1
    • Check the distribution of platform_os across experiment groups in the AdSmart dataset.
  • 2
    • Check the distribution of gender across checkout_page designs in the checkout dataset.