A new paper from ACM warns that improper randomization can invalidate entire A/B testing experiments. The research shows how subtle implementation errors in randomization algorithms lead to biased results that practitioners may not detect. When randomization fails, metrics become unreliable and business decisions based on those experiments could be fundamentally flawed.

Randomization serves as the foundation of experimental design, ensuring treatment and control groups are comparable. The paper identifies common pitfalls including pseudo-random number generator flaws, time-based biases, and selection bias in user assignment. These issues can create systematic differences between groups that masquerade as treatment effects. Even statistically significant results may be completely meaningless if the underlying randomization is compromised.

The authors provide concrete examples of how improper randomization has led to false positives in real-world experiments. They emphasize that proper randomization requires careful attention to implementation details and ongoing monitoring. Practitioners should validate their randomization procedures through statistical tests and maintain rigorous documentation. The paper concludes that without robust randomization, the entire experimental framework collapses, making it impossible to draw valid conclusions from A/B tests.