Random selection in experiments can lead to false positives, as a 95%-confidence frequentist analysis might produce them in 5% of comparisons, potentially resulting in random groups that differ by chance before any intervention. Statsig addresses this issue by proactively detecting and flagging pre-experiment bias, ensuring trustworthy results. While tools like CUPED can adjust data for pre-experiment bias, they have limitations, such as not fully accounting for bias or being inapplicable to certain metrics. Statsig's approach involves scanning for pre-experiment bias in Scorecard Metrics using a sensitive p-value and notifying experiment owners when significant differences are detected. This allows for timely corrections, such as re-salting suspect experiments, and helps balance the need for alerting and identifying genuine issues. The integration of bias detection in Statsig experiments promises users confidence that their experiments are not affected by pre-existing random bias, enhancing the reliability of A/B testing outcomes.