A/B Test Analysis

Evaluate A/B test results with statistical rigor and translate findings into clear product decisions.

Context

You are analyzing A/B test results for $ARGUMENTS.

If the user provides data files (CSV, Excel, or analytics exports), read and analyze them directly. Generate Python scripts for statistical calculations when needed.

Instructions

Understand the experiment:

• What was the hypothesis?

• What was changed (the variant)?

• What is the primary metric? Any guardrail metrics?

• How long did the test run?

• What is the traffic split?

Validate the test setup:

• Sample size: Is the sample large enough for the expected effect size?

• Use the formula: n = (Z²α/2 × 2 × p × (1-p)) / MDE²

• Flag if the test is underpowered (<80% power)

• Duration: Did the test run for at least 1-2 full business cycles?

• Randomization: Any evidence of sample ratio mismatch (SRM)?

• Novelty/primacy effects: Was there enough time to wash out initial behavior changes?

Calculate statistical significance:

• Conversion rate for control and variant

• Relative lift: (variant - control) / control × 100

• p-value: Using a two-tailed z-test or chi-squared test

• Confidence interval: 95% CI for the difference

• Statistical significance: Is p < 0.05?

• Practical significance: Is the lift meaningful for the business?

If the user provides raw data, generate and run a Python script to calculate these.

Check guardrail metrics:

• Did any guardrail metrics (revenue, engagement, page load time) degrade?

• A winning primary metric with degraded guardrails may not be a true win

Interpret results:

Outcome Recommendation

Significant positive lift, no guardrail issues Ship it — roll out to 100%

Significant positive lift, guardrail concerns Investigate — understand trade-offs before shipping

Not significant, positive trend Extend the test — need more data or larger effect

Not significant, flat Stop the test — no meaningful difference detected

Significant negative lift Don't ship — revert to control, analyze why

Provide the analysis summary:

A/B Test Results: [Test Name]

Hypothesis: [What we expected] Duration: [X days] | Sample: [N control / M variant]

Recommendation: [Ship / Extend / Stop / Investigate] Reasoning: [Why] Next steps: [What to do]

Think step by step. Save as markdown. Generate Python scripts for calculations if raw data is provided.

Further Reading

• A/B Testing 101 + Examples

• Testing Product Ideas: The Ultimate Validation Experiments Library

• Are You Tracking the Right Metrics?