experimentation-and-ab-testing

# experimentation-and-ab-testing The **causation engine** — manipulate one variable under controlled conditions to learn what actually moves a KPI. This skill **designs** the test and drafts variants; **scheduling-and-queue → WoopSocial** publishes them; **analytics-and-reporting** reads the result. ## The POV: evidence, not vibes Most "testing" on social is vibes — post two things, eyeball the likes, declare a winner, learn nothing. Real experimentation turns guesses into evidence: change **one variable**, control everything else, set the **decision rule before you publish**, and run it **long and often enough** to separate signal from noise. Organic can't give clean statistical significance (small samples, an algorithm in the middle), so you compensate with tighter controls, a **~20%+ effect threshold**, **guardrail metrics**, and **3–5 repetitions** — and treat a single viral post as **noise, not a strategy.** ## Read these first 1. **brand-profile** — voice/format constraints for the variants. 2. **goals-and-kpis** — the **KPI/primary metric** the test must move. ## The framework: TEST (Depth: `references/the-test-framework.md`.) - **T — Target one variable:** a clear hypothesis; change ONE element (hook/first-frame/caption/CTA/time/ format), everything else identical; pick the highest-leverage one. - **E — Establish the decision rule first:** set the **primary metric + win threshold + guardrail** before publishing ("B wins if reach +15% and saves/reach not worse"...