At this point in the experiment, nothing you can do can correct for the sampling group differences to make them equal. The only thing you can do is make assumptions about how the two groups will behave.

If you can safely assume that previous treatments that you have applied using the even/odd sampling method will not interact with the new A/B test, then a difference-in-differences method would be appropriate.

As a simple example of when this assumption (and a difference-in-differences) is inappropriate: Let's say in your first test you send group A (the odds) a book titled "The importance of eating healthy." In the second test, you send group A (the odds) dietary supplements. Group A has been compromised by prior treatments that could impact the effects on the second test.

At this point in the experiment, nothing you can do can make the two groups equal. What you can do is make assumptions about how the two groups will behave.

If you can safely assume that previous treatments that you have applied using the even/odd sampling method will not interact with the new A/B test, then a difference-in-differences method would be appropriate.

As a simple example of when this assumption (and a difference-in-differences) is inappropriate: Let's say in your first test you send group A (the odds) a book titled "The importance of eating healthy." In the second test, you send group A (the odds) dietary supplements. Group A has been compromised by prior treatments that could impact the effects on the second test.

At this point in the experiment, nothing you can do can correct for the sampling group differences to make them equal. The only thing you can do is make assumptions about how the two groups will behave.

If you can safely assume that previous treatments that you have applied using the even/odd sampling method will not interact with the new A/B test, then a difference-in-differences method would be appropriate.

At this point in the experiment, nothing you can do can correct for the sampling group differences to make them equal. The only thing you can do is make assumptions about how the two groups will behave.

If you can safely assume that previous treatments that you have applied using the even/odd sampling method will not interact with the new A/B test, then a difference-in-differences method would be appropriate.

As a simple example of when this assumption (and a difference-in-differences) is inappropriate: Let's say in your first test you send group A (the odds) a book titled "The importance of eating healthy." In the second test, you send group A (the odds) dietary supplements. Group A has been compromised by prior treatments that could impact the effects on the second test.