Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
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Evaluate A/B test results with statistical rigor and translate findings into clear product decisions.
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.
Understand the experiment:
Validate the test setup:
Calculate statistical significance:
If the user provides raw data, generate and run a Python script to calculate these.
Check guardrail metrics:
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]
| 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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概要と使いどころ
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日本語の概要は準備中です。原文の説明を表示しています。
Generate an Ansoff Matrix analysis mapping growth strategies across market penetration, market development, product development, and diversification. Use when considering growth options, planning market expansion, or evaluating strategic growth paths.
日本語の概要は準備中です。原文の説明を表示しています。
Identify the first beachhead market segment for a product launch. Evaluates segments against burning pain, willingness to pay, winnable market share, and referral potential. Use when choosing a first market, targeting an initial customer segment, or planning market entry strategy.
日本語の概要は準備中です。原文の説明を表示しています。
Design experiments to test assumptions for an existing product — prototypes, A/B tests, spikes, and other low-effort validation methods. Use when validating assumptions, testing feature ideas cheaply, or planning product experiments.
日本語の概要は準備中です。原文の説明を表示しています。
Design lean startup experiments (pretotypes) for a new product. Creates XYZ hypotheses and suggests low-effort validation methods like landing pages, explainer videos, and pre-orders. Use when validating a new product idea, creating pretotypes, or testing market demand.
日本語の概要は準備中です。原文の説明を表示しています。
Brainstorm product ideas for an existing product using multi-perspective ideation from PM, Designer, and Engineer viewpoints. Use when generating new feature ideas, brainstorming solutions for an identified opportunity, or ideating with a product trio.
日本語の概要は準備中です。原文の説明を表示しています。