The Crystal-Lang developers have a fundraiser going and Matz (creator of Ruby) has already contributed $500:
98 karma · joined December 9, 2011
Suppose I have a website with a "Click Me" button that's green in color. I want to increase clicks and think to myself, "perhaps if it was a red button instead of a green button, more people would click!" To test this, I would run an A-B test along the lines of:
if random(2) == 0 then color='red' else color='green';
In theory, I just push this code and track the number of clicks on the red button versus the green button and then pick the best. But in practice, when I push the code, there might be 5 clicks on green and none on red in the first hour. Maybe green is better? Maybe I didn't wait long enough? Okay, let's wait longer. A few hours later, there's now 10 clicks on red and only 6 clicks on green. Okay, so red is better? Let's wait even longer. A week later, there's 5000 clicks on red and 4500 clicks on green. That seems like enough data that I can make a conclusion about red vs. green. But is there a better way?
This is where A-A-B-B testing can help. Let's start by looking at just the A-A part of the test. If I split my audience into two groups (green1 and green2) and show them both green buttons, the results should be identical because both buttons are green. If I check back in an hour and the "green1" and the "green2" groups are off by 20%, then I have a large margin of error and need to wait longer. If I check back in 6 hours and they're off by 10%, then I need to wait longer. If I check back in a day and green1 and green2 are only off by 1% then that means we've probably waited long enough and my margin of error is around 1%. I can now add green1+green2 and compare it to red1+red2 groups and see if there's a clear winner (e.g. red is 5% better). And this only took a day instead of a week!