Common Pitfalls of A/B Testing

There are several potential pitfalls in A/B testing.

One common issue is unbalanced groups, which can lead to skewed results if dimensions such as user demographics or devices are not evenly distributed.

Another is Type I and Type II errors:

• Type 1: false positives occur when the null is incorrectly rejected
• Type 2: false negatives occur when a real effect is missed.

Experiments that are too short may not capture representative behavior, leading to inaccurate conclusions due to low power.

Seasonal effects or unusual periods (like holidays) can further skew results.

Running too few tests or not accounting for interaction effects when running multiple tests can also result in flawed interpretations.

Ensuring correct experiment duration and group balancing is crucial.