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  3. A/B Testing in Python

Exercise

Pairwise t-tests

Manually running separate comparisons using individual t-tests can be a pain as the number of groups gets larger. Thankfully, the pingouin package's .pairwise_tests() method can make things easier.

You will explore the differences in average time-on-page metric between four different landing page variants loaded in the homepage DataFrame.

The dataset homepage is available and has the columns signup and time_on_page. Every row in the DataFrame corresponds to a unique user visiting the respective landing_page. The signup column consists of binary data: '1' means the user signed up and '0' means abandoned the page, and the time_on_page column represents the time (in seconds) each user spent on the landing page before abandoning or signing up . pingouin has been loaded along with pandas and numpy.

Instructions 1/3

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  • Perform pairwise t-tests on homepage's time_on_page variable, grouped by landing_page, without doing any p-value adjustment.