You’re running a test on one of your landing pages with the primary goal of getting the user to click on a button. You are split testing (A/B testing) two different button colors (green and orange) to determine the impact of the button color on click-through rate. You collect the following initial results:
Button Color |
Visitors |
Clicks |
CTR (y) |
Green |
38 |
2 |
5.2% |
Orange |
39 |
3 |
7.7% |
The orange button is better, right?
Not necessarily. Sure, the orange button has a 7.7% click-through rate (CTR) compared to only 5.2% for the green button. However, the orange button has really only earned one more click. If the next visitor on the page clicks on the green button, both variants will have a 7.7% CTR, indicating that button color is irrelevant in this application.
Here is what happens if we run this experiment for several months with 16,000+ visitors:
Button Color |
Visitors |
Clicks |
CTR (y) |
Green |
8,238 |
486 |
5.9% |
Orange |
7,893 |
734 |
9.3% |
Is the orange button better now? Hell yeah, it is.
While the danger of making decisions based on too little data is an incorrect conclusion, the danger in collecting too much data is a waste of time, effort and money. Even though there was little data to determine orange was best in the first variant, had you decided to go with the orange button anyway, you could have sent all the above 16,000+ visitors to the page that performs at a 9.3% CTR. Thus, giving you a whole lot more sales/leads/etc.
So how many users are required to make this decision? We can use the following equation:
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