ABTests

ABTests, commonly referred to as A/B tests, are controlled experiments used to compare two or more variants of a product or feature to determine which performs better on a predefined metric. In a typical AB test, users are randomly assigned to a control variant (A) or one or more treatment variants (B, C, etc.). The goal is to identify meaningful differences in outcomes such as conversion rate, click-through rate, engagement, or revenue per user.

Before running an experiment, a hypothesis is stated and a sample size is estimated to achieve adequate statistical power for detecting a minimum detectable effect. After data collection, results are analyzed for statistical significance using p-values and confidence intervals, or Bayesian posterior probabilities. If the treatment shows a meaningful improvement beyond a predefined threshold, it may be deployed; otherwise, the original variant remains in use.

AB testing has origins in the science of designed experiments and was refined by early statisticians, with digital AB testing gaining prominence through online experimentation platforms in the 2000s. Key considerations include maintaining randomization integrity, avoiding biased peeking, and planning for multiple comparisons when several variants are tested. Some methods use sequential or adaptive testing, which requires appropriate stopping rules to control error rates.

Common metrics for evaluation include conversion rate, click-through rate, engagement, revenue per user, and lifetime value. Limitations include potential non-representativeness of the sample, external factors influencing results, and the assumption that effects are constant across users and contexts. AB testing is widely used in marketing, product development, and user experience optimization to support evidence-based decision making.