Was this a One-tailed Test?
First of all, I think your statistical test is giving you a 1-tailed p-value, rather than a 2-tailed p-value that you should use in what sounds like exploratory work. I think you’re saying your p-value is 0.02 (i.e., there is a 2% chance of getting the observed difference in conversions by random luck). However, if the number of visitors to your control condition is about the same as the variation, it should be closer to the 0.04 to 0.05 range (I can’t calculate the exact value because (a) I need to know the number of visits and conversions for the control, and (b) a sample size of nearly 50,000 per variation blows the mind of my little ol’ home-made Fisher Exact calculator).
Elevated Type I Error?
Still, the p-value is low enough in my book that it’s worth believing that you have a systematic rather than random effect… except that it sounds like you’re doing a lot of tests. The way inferential statistics works, 1 in 20 variations that in fact have no real effect will come out “statistically significant” on average. Such an event is called a Type I error. It implies that if you test a lot of variations for effects they really should not have, you need to expect that one in twenty will show a spurious effect.
So did you do 20 tests? Is this exactly what you should expect to happen if all your variations in fact do nothing on anything? Even if you didn’t do 20 tests, the more tests you do, the more you raise the chance of one or more of them having a Type I error. For example, it seems you did three tests to compare three variations with the control on the primary goal, plus three additional tests for each variation on the unrelated goal, for at least six tests in total. If in fact none of your variations affect anything, you’d have a 0.26 chance of at least one coming out “statistically significant.” That’s a pretty high chance. If you did 15 tests (e.g., 3 variations tested on 5 goals), you’d have a 0.54 chance –you probably will get at least one spurious result. My guess is that’s what’s happening here.
In any case, if this is a real effect, our best guess is we'll get only about 40 more conversions per 50,000 visitors. It may literally not be worth the cost of moving the winning variation to production. Whether it is or not depends the number of visitors you get per month, the profit from each conversion, and how much work it is to put the variation into production. You should be able to calculate how many months it’ll take until it pays off. If it takes years, I wouldn’t bother.
Potential Lesson Learned
The lesson may be you can’t blindly trust what on-line A-B testing services tell you. Many of them give you only an approximately correct (i.e., wrong) p-value. In addition to giving only one-tailed values, they often force you to test only one variation against a control at a time, increasing the number of tests and therefore the chance of a spurious result. There are pretty simple and commonly known procedures to test all variations against the control (and each other) all at once on a given goal that yields a single p-value (Chi-square or G-test for independence with more than 2 columns), but on-line services don’t give you that option. There is also a simple adjustment, called the Bonferroni Correction, you can apply to tests for multiple goals that controls for these spurious results (I can tell you that if you apply the correction to your data it would no longer be close to significant any more).
I discuss some of the errors you see in on-line A-B tests at Stat 203. For a non-mathematical intro to stats for user performance testing, see Stat 101.