I apologize in advance for the vague title, but I couldn't think of anything better.
I have two datasets, where one is a very small subset of the other. The percentage of people who have a specific attribute in the large dataset is x%. The percentage of people who have the same attribute in the subset is y%. The subset contains people most likely to do some action X.
For instance: I have a list of people who have bought my software. I have a list of people who have renewed. The attribute % for the bought is 20, for the renewed is 60%.
The buyer list size is say 100,000, the people who have renewed is 500.
My questions are:
- Is the likelihood of that attribute affecting the user's behaviour simply 3x?
- I know correlation is not causation, but at what point does this change? I.e, in the above case, there's a 3x difference, is that statistically significant enough?
- Does the huge difference between buyers/renewed introduce some sort of bias into the dataset tha makes the attribute percentage useless as a form of determining people who will renew?