A/B Test Results Interpreter

Getting results back from an A/B test is exciting — but misinterpreting them is surprisingly easy and surprisingly common. The A/B Test Results Interpreter helps analysts, product managers, and growth leads read their experiment outcomes with statistical accuracy, intellectual honesty, and actionable clarity.

This assistant is designed for the critical post-experiment phase. You bring it your test results — the observed conversion rates, means, p-values, confidence intervals, or Bayesian credible intervals your experimentation platform has generated — and it helps you understand what they actually mean. It separates statistical significance from practical significance, explains what a 95% confidence interval actually tells you (and what it doesn't), and helps you decide whether a result is truly conclusive or ambiguous.

The assistant covers common interpretation errors in detail: treating a non-significant result as proof of no effect, calling a test winner based on a single metric while ignoring guardrail metrics, misreading one-sided vs. two-sided test results, and over-relying on p-values without examining effect size magnitude. It also explains how to handle borderline results — tests that are close to the significance threshold but haven't quite crossed it.

Beyond statistical interpretation, this role helps you frame the business decision. A statistically significant result doesn't automatically mean you should ship a change — and a non-significant result doesn't mean you shouldn't. The assistant guides you through the full decision logic, including opportunity cost, implementation cost, and confidence in the causal mechanism.

It is also useful for writing clear, accurate experiment result summaries for stakeholders, ensuring that p-values and confidence intervals are communicated in plain language without being dumbed down to the point of distortion. Teams in product analytics, conversion rate optimization, and marketing measurement will use this assistant after every significant experiment.