Write a Testable A/B Test Hypothesis
Turn a vague idea into a testable A/B hypothesis with a clear assumption, metric and expected effect.
Last updated: July 2026 · Collective Brain
Good for
- Make a landing page change testable before you set it up in your tool
- Translate a backlog full of ideas into hypotheses you can prioritize
- Define upfront what result confirms or kills the idea
The prompt
You are an experienced conversion and product analyst focused on clean A/B tests.
Context:
- Product or page: [your product or page]
- Planned change: [what you want to change]
- Target metric: [e.g. click rate on the CTA, checkout completion rate]
Task: Turn these inputs into a clean, testable A/B test hypothesis. Think like someone stress-testing the idea before the test, not someone selling it.
Return exactly this structure:
1. Hypothesis in one sentence: "If we [change], then [expected effect on the metric], because [assumption about user behavior]."
2. Assumption under test: the underlying belief about users, in one sentence.
3. Primary metric: the single number that decides success.
4. Secondary and guardrail metrics: what else you watch so you do not miss a side effect.
5. Expected direction and rough size of the effect, clearly flagged as an assumption, not a promise.
6. Stop and success criteria: at what result you roll out the variant, kill it, or keep watching.
If you lack details needed to sharpen the hypothesis, ask me up to three targeted questions first. Do not invent numbers for confidence or significance.Replace the bracketed placeholders with your own details.
Frequently asked
How do I know my hypothesis is actually testable?
A testable hypothesis names a concrete change, a measurable metric and an expected direction of effect. If you cannot clearly say afterwards whether it held up, it was too vague. This prompt forces those three parts into one structure.
Can ChatGPT decide whether my test is significant?
No, and the prompt deliberately avoids pretending it can. ChatGPT helps you frame the hypothesis cleanly, but it never sees your real test data. Confidence and statistical significance are calculated in your A/B testing tool or with a significance calculator.
Related
Rather have it done?
Prompts are a start.
Results are our job.
When the prompt should turn into real work that holds up consistently across every channel, we take over. Start free, finish professionally.