Experimentation
Experiments can include A/B tests, multivariate tests, holdouts, or other controlled designs. Good experimentation begins with a decision-relevant hypothesis, clear eligibility rules, primary and guardrail metrics, and a plan for sample size and duration. The result should inform a decision even when no statistically reliable lift is found. Teams should document learnings and avoid treating every correlation as causal evidence.
What is Experimentation?
Experimentation is a structured process for testing hypotheses by changing an experience and measuring the effect on defined outcomes.
Why it matters
Experiments can include A/B tests, multivariate tests, holdouts, or other controlled designs. Good experimentation begins with a decision-relevant hypothesis, clear eligibility rules, primary and guardrail metrics, and a plan for sample size and duration. The result should inform a decision even when no statistically reliable lift is found. Teams should document learnings and avoid treating every correlation as causal evidence.
Example
A team tests whether shortening account setup improves activation without increasing fraud, support contacts, or early churn.