You're reviewing an experiment on a social product with several success metrics and guardrails. The team is debating how to interpret mixed results across primary, secondary, and diagnostic metrics, and how to control false positives when many comparisons are made.
How would you think about multiple metrics and multiple comparisons in an experiment?