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Experiment readout: from raw results to ship / iterate / kill

The A/B test ended. Write the readout that survives the skeptic in the room — validity checks, segment cuts, and a labeled-confidence recommendation.

The prompt — copy and run it

You are a product analyst writing an experiment readout. I will paste the design and the results. Produce:

A) READOUT — the hypothesis as originally registered, primary-metric result with its confidence interval, guardrail metrics, and actual duration/sample vs plan.

B) VALIDITY CHECKS — a table: check (sample-ratio mismatch, novelty effect, seasonality overlap, peeking/early stopping, segment reversal), status (pass / fail / cannot assess), and the evidence.

C) DECISION — ship / iterate / kill, with confidence labeled high/med/low and the reasoning in 3 sentences. If iterate: the single next test.

Inputs: [EXPERIMENT DESIGN + REGISTERED SUCCESS CRITERIA] · [PASTE RESULTS: METRICS, SAMPLE SIZES, INTERVALS OR P-VALUES] · [KEY SEGMENTS]

Rules: Do not invent statistics or infer significance the data does not support — if the model cannot compute it from my paste, say "insufficient data"; that is a valid readout result. Verify metric definitions with the analytics team before publishing. Keep user-level and confidential data out of the readout.

How to use this prompt

  1. Copy the full prompt above with the Copy button.
  2. Fill in your inputs. Replace each bracketed placeholder with your specifics: [EXPERIMENT DESIGN + REGISTERED SUCCESS CRITERIA][PASTE RESULTS: METRICS, SAMPLE SIZES, INTERVALS OR P-VALUES][KEY SEGMENTS]
  3. Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.

Why this prompt works

Most experiment 'wins' die under three questions: was the sample ratio right, did you peek, does it hold by segment. Building the validity table into the readout means you ask them before your CPO does — and labeled confidence keeps a marginal result from shipping as a sure thing.

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Reality guardrail: this prompt makes the model reason from data you paste — it does not source or verify facts for you. Check every claim, keep confidential data out of consumer AI tools, and follow your employer's AI-use policy.

Frequently asked

When should I use this prompt?

The A/B test ended. Write the readout that survives the skeptic in the room — validity checks, segment cuts, and a labeled-confidence recommendation.

Why does this prompt work?

Most experiment 'wins' die under three questions: was the sample ratio right, did you peek, does it hold by segment. Building the validity table into the readout means you ask them before your CPO does — and labeled confidence keeps a marginal result from shipping as a sure thing.

What mistake does this prompt help you avoid?

Marginal A/B 'wins' shipping as sure things — sample-ratio, peeking, and segment checks built into the readout with labeled confidence.

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PromptSharp prompts are drafted with AI assistance and human-reviewed. They structure how a model reasons over data you provide — they do not source or verify facts for you, and you own every output. Nothing here is financial, legal, tax, or investment advice. Never paste confidential, client, or material non-public information into consumer AI tools; follow your employer's AI-use policy. © 2026 PromptSharp.