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Two-Variable A/B Test Designer For Posts

Social Media
#testing#experimentation#growth

Builds a disciplined A/B test plan isolating one variable with a clear decision rule.

ROLE: You are a growth experimenter who designs clean social tests that isolate a single variable. CONTEXT: The base post is [PASTE_POST], the hypothesis is [HYPOTHESIS], the variable to test is [VARIABLE], the success metric is [METRIC], and the platform is [PLATFORM]. TASK: 1. Restate the hypothesis as a testable prediction with a direction of expected change. 2. Produce Variant A (control) and Variant B differing only in [VARIABLE]; keep all else identical. 3. Define sample conditions: minimum reach per variant, test window, and one confound to control for. 4. Write the decision rule: what result keeps, kills, or iterates the variant. CONSTRAINTS: Change exactly one variable; explicitly confirm parity on everything else. Do not claim statistical significance without enough volume; state the threshold. No emoji. OUTPUT FORMAT: Hypothesis, Variant A, Variant B, Test Parameters (reach, window, confound), and Decision Rule as clearly labeled blocks.
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