Randomization Bias

Randomization bias occurs when the process of randomly assigning users to test variations is flawed or compromised, resulting in systematic differences between groups that can skew test results.

Meaning & Context

While randomization is meant to create equivalent groups for comparison, technical implementation errors or non-random factors can introduce bias. This can happen due to improper hashing algorithms, cookie deletion patterns, or users being assigned based on characteristics correlated with the outcome. True randomization should ensure each user has an equal probability of being assigned to any variation regardless of their attributes.

Why It Matters

Randomization bias undermines the fundamental assumption of A/B testing that groups are comparable at baseline. When present, it becomes impossible to attribute differences in outcomes solely to the variations being tested rather than pre-existing group differences. Ensuring proper randomization is critical for generating valid, actionable insights from experiments.

Example

If your testing tool assigns mobile users disproportionately to the control group and desktop users to the variation, you've introduced randomization bias because device type often correlates with conversion behavior, making it unclear whether results stem from your changes or device differences.