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Exercise

Sample size for proportions

Real datasets can be messy. As an Analytics Engineer working with data in the real world, you are going to encounter situations where the metric of interest is too noisy and the variance in the data is too high to be able to capture a meaningful difference. This problem is more likely to happen with continuous metrics such as the average order value in the previous exercise. There are several ways to tackle this issue, but one of the workarounds a clever engineer would resort to is finding a proxy metric that has lower variance but still aligns with the business goals.

In this exercise, you will look at calculating the sample size for a binary metric; signup rate which represents whether a user signed up for the service or not, as opposed to the paid price which may vary more between users. The homepage DataFrame and and pandas, numpy libraries are already imported and loaded for you.

Instructions 1/3
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  • Calculate the signup rate of landing_page 'A'.
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