At its most fundamental level, A/B testing just involves creating two different versions of a web page. Sometimes, the changes are major redesigns of the site or the user experience, but usually, the changes are as simple as changing the text on a button. Then, for a short period of time, new visitors are randomly shown one of the two versions of the page. The site tracks their behavior, and the experiment determines whether one version or the other increases the users' interaction with the site. This may mean more click-through, more purchases, or any other measurable behavior.

This is similar to other methods in other domains that use different names. The basic framework randomly tests two or more groups simultaneously and is sometimes called random-controlled experiments or online-controlled experiments. It's also sometimes referred to as split testing, as the participants are split into two groups.

These are all examples of between-subjects experiment design. Experiments that use these designs all split the participants into two groups. One group, the control group, gets the original environment. The other group, the test group, gets the modified environment that those conducting the experiment are interested in testing.

Experiments of this sort can be single-blind or double-blind. In single-blind experiments, the subjects don't know which group they belong to. In double-blind experiments, those conducting the experiments also don't know which group the subjects they're interacting with belong to. This safeguards the experiments against biases that can be introduced by participants being aware of which group they belong to. For example, participants could get more engaged if they believe they're in the test group because this is newer in some way. Or, an experimenter could treat a subject differently in a subtle way because of the group that they belong to.

As the computer is the one that directly conducts the experiment, and because those visiting your website aren't aware of which group they belong to, website A/B testing is generally an example of double-blind experiments.

Of course, this is an argument for only conducting the test on new visitors. Otherwise, the user might recognize that the design has changed and throw the experiment away. For example, the users may be more likely to click on a new button when they recognize that the button is, in fact, new. However, if they are new to the site as a whole, then the button itself may not stand out enough to warrant extra attention.

In some cases, these subjects can test more variant sites. This divides the test subjects into more groups. There needs to be more subjects available in order to compensate for this. Otherwise, the experiment's statistical validity might be in jeopardy. If each group doesn't have enough subjects, and therefore observations, then there is a larger error rate for the test, and results will need to be more extreme to be significant.

In general, though, you'll want to have as many subjects as you reasonably can. Of course, this is always a trade-off. Getting 500 or 1000 subjects may take a while, given the typical traffic of many websites, but you still need to take action within a reasonable amount of time and put the results of the experiment into effect. So we'll talk later about how to determine the number of subjects that you actually need to get a certain level of significance.

Another wrinkle that is you'll want to know as soon as possible is whether one option is clearly better or not so that you can begin to profit from it early. In the multi-armed bandit problem, this is a problem of exploration versus exploitation. This refers to the tension in the experiment design (and other domain) between exploring the problem space and exploiting the resources you've found in the experiment so far. We won't get into this further, but it is a factor to stay aware of as you perform A/B tests in the future.

Because of the power and simplicity of A/B testing, it's being widely used in a variety of domains. For example, marketing and advertising make extensive use of it. Also, it has become a powerful way to test and improve measurable interactions between your website and those who visit it online.

The primary requirement is that the interaction be somewhat limited and very measurable. Interesting would not make a good metric; the click-through rate or pages visited, however, would. Because of this, A/B tests validate changes in the placement or in the text of buttons that call for action from the users. For example, a test might compare the performance of Click for more! against Learn more now!. Another test may check whether a button placed in the upper-right section increases sales versus one in the center of the page.

These changes are all incremental, and you probably don't want to break a large site redesign into pieces and test all of them individually. In a larger redesign, several changes may work together and reinforce each other. Testing them incrementally and only applying the ones that increase some metric can result in a design that's not aesthetically pleasing, is difficult to maintain, and costs you users in the long run. In these cases, A/B testing is not recommended.

Some other things that are regularly tested in A/B tests include the following parts of a web page:

Now that we have an understanding of what A/B testing is and what it can do for us, let's see what it will take to set up and perform an A/B test.