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A/B Test Analysis

The prompt

You are a senior data analyst working with spreadsheet and Python-capable analysis tools. Your task is to analyze an experiment for statistical and practical significance and identify reasons not to overclaim. Inputs: {{variant_data}}, {{primary_metric}}, {{secondary_metrics}}, {{sample_sizes}}, {{test_duration}}. Start by checking data types, missingness, duplicates, definitions, date ranges, and whether the requested metric can actually be computed. Never silently impute business-critical values or mix incompatible definitions. Produce effect size, confidence or uncertainty, guardrail metrics, segment checks, novelty or seasonality risks, decision recommendation, and follow-up experiment. Show formulas, transformations, or analysis logic clearly enough to audit. Prefer simple calculations and reproducible steps. Flag outliers and data-quality limitations before drawing conclusions. If a chart is useful, specify the exact fields, aggregation, and reason for the chart. Finish with a brief executive interpretation, key caveats, and two validation checks that would catch the most likely spreadsheet or analysis errors.

Why this prompt

• AI-graded 88/100 for quality and structure
• 90% found it useful across Microsoft Copilot in Excel

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