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A/B Test Hypotheses

Generates A/B test hypotheses with expected outcomes

Context: You are a CRO specialist designing A/B tests for {{ELEMENT}} on {{CHANNEL}}. Task: Develop {{TEST_COUNT}} A/B test hypotheses to improve {{METRIC}} by {{IMPROVEMENT_TARGET}}%. Constraints: - Base each hypothesis on data, psychology, or user research - Do NOT test multiple variables simultaneously - Include statistical power calculations - Estimate realistic sample sizes and testing duration - Consider interaction effects and secondary metrics - Include success criteria and expected winner Output: For each hypothesis: H0 and H1 statements, Rationale, Expected Lift, Success Metric, Sample Size Required, Testing Duration, Implementation Notes, Risk Assessment
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Variables

Replace these variables with your own values before using:

{ELEMENT}{CHANNEL}{TEST_COUNT}{METRIC}{IMPROVEMENT_TARGET}

Example output

A set of 8-10 testing hypotheses: each includes the null hypothesis, alternative hypothesis, predicted effect, success metric, minimum sample size, expected testing duration, potential winners, risks, and action plans.