Ab Test Analysis

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

Phase 0: Context Confirmation

Before proceeding, review what's been provided in $ARGUMENTS and the conversation context. Only ask about what's missing — don't re-ask what's already clear.

1. Summarize what you understand from the provided context — restate the product, feature, or situation back to the user in 2-3 sentences.
2. Identify gaps — if any of the following are unclear, ask:
   • What was the hypothesis being tested?
   • What is the primary metric and any guardrail metrics?
   • How long did the test run and what was the traffic split?
3. Confirm: "Here's my understanding: [summary]. I plan to [brief description of what the skill will produce]. Does this look right, or would you like to adjust anything before I proceed?"

If the user provides additional context, incorporate it. If the user confirms, proceed.

1. 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?

2. 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?

3. 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.

4. 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

5. 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

6. Provide the analysis summary:

   ## A/B Test Results: [Test Name]
   
   **Hypothesis**: [What we expected]
   **Duration**: [X days] | **Sample**: [N control / M variant]
   
   | Metric | Control | Variant | Lift | p-value | Significant? |
   |---|---|---|---|---|---|
   | [Primary] | X% | Y% | +Z% | 0.0X | Yes/No |
   | [Guardrail] | ... | ... | ... | ... | ... |
   
   **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.

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