What is an A/B Test?

With many experiments, including the Upside Down box art example, we need to think carefully about what our metrics are telling us. Suppose we look at the click-through rate, measuring the fraction of members in each experience that clicked on a title. This metric alone may be a misleading measure of whether this new UI is a success, as members might click on a title in the Upside Down product experience only in order to read it more easily.

Because we create our control (“A”) and treatment (“B”) groups using random assignment, we can ensure that individuals in the two groups are, on average, balanced on all dimensions that may be meaningful to the test. Random assignment ensures, for example, that the average length of Netflix membership is not markedly different between the control and treatment groups, nor are content preferences, primary language selections, and so forth. 

A/B tests let us make causal statements. We’ve introduced the Upside Down product experience to Group B only, and because we’ve randomly assigned members to groups A and B, everything else is held constant between the two groups. We can therefore conclude with high probability (more on the details next time) that the Upside Down product caused the reduction in engagement.

With the Top 10 example, the hypothesis read: “Showing members the Top 10 experience will help them find something to watch, increasing member joy and satisfaction.” The primary decision metric for this test (and many others) is a measure of member engagement with Netflix: are the ideas we are testing helping our members to choose Netflix as their entertainment destination on any given night? Our research shows that this metric (details omitted) is correlated, in the long term, with the probability that members will retain their subscriptions.