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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.
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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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?
{'code': 'PF05', 'note': "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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