Chance to Beat Control (CTBC) determines statistical significance and shows the probability that the variation page will perform better than the control version. E.g., a CTBO of 95% means that the variation is likely to perform better than the control version 95% of the time.
The Chance to Beat Control is typically calculated using statistical analysis, specifically Bayesian methods or Frequentist methods. One of the common approaches is Bayesian statistics, which incorporates prior knowledge and continually updates beliefs as data is collected during the experiment.
The formula for calculating the Bayesian Chance to Beat Control is as follows:
Chance to Beat Control = ∫ [ P(θv > θc | D) P(θv | D) dθv ]
Where:
Let’s consider an A/B test where a marketing team is comparing two different email subject lines (Variation vs. Control) to determine which one leads to a higher open rate. After running the experiment and collecting data from a sample of users, the team uses Bayesian analysis to calculate the Chance to Beat Control. If the Chance to Beat Control is 90%, it means there’s a 90% probability that the Variation will outperform the Control in terms of open rates.