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These data are from an A/B test of free trials to conversion. Someone can sign up for a 30 day free trial of the product. If they like it they stay on and become paying customers. By default (A), when someone signs up they receive a 6 part email sequence over the 30 days. Group B, the test, received a 16 part sequence.

I would like to know if the difference in conversion rate for group by really is higher, or if there's a good chance it would have happened anyway with the natural ebbs and flows around the mean.

From Googling around and previous posts on this forum I wanted to use a chi-squared test. But my lecturer told me to test for a difference in proportions. Now I'm just confused.

Which method would be appropriate to test if the difference between the two groups is sufficient to arrive at any conclusions about one group being better than the other?


As noted in the linked thread, the chi-squared test and the z-test of equal proportions are ultimately the same test. However, not everyone knows that, and the names differ. I will typically use the name "z-test of equal proportions" in discussions and write-ups, or recommend it to people (see, e.g.: What is the difference between McNemar's test and the chi-squared test, and how do you know when to use each?). I do this because I think it makes the way you are thinking about your data clearer.

  • $\begingroup$ @gung +1 Still working on the topic, as you can see... This comment clarifies a lot! $\endgroup$ Aug 19 '15 at 17:02

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