Use case 01
Use when a product, growth, or operations team wants to test a change rigorously.
Business AnalystAB Testing and ExperimentationIntermediateSingle prompt
Inconclusive Test Diagnosis AI Prompt
This prompt helps design, size, analyze, or govern experiments in a structured way. It is useful when a team wants to make product or process decisions based on evidence instead of opinion. The output should balance statistical rigor with practical business judgment so stakeholders can act confidently. It explains why a test may have failed to reach significance and what to do next.
Prompt text
This A/B test returned an inconclusive result (p > 0.05, no significant effect detected). Diagnose why and recommend next steps. 1. Check statistical power: - Was the test adequately powered? Calculate post-hoc power given observed effect size and sample size. - If power < 80%, the test was underpowered — this is likely a false negative, not proof of no effect. 2. Check the effect size: - What was the observed effect size, even if not significant? - Is the observed effect smaller than the MDE? If yes, the test was powered for a larger effect. 3. Check test duration: - Was the test run long enough to cover at least one full weekly cycle? - Was the test affected by external events (seasonality, promotions, product launches)? 4. Check for segment heterogeneity: - Does the effect appear in specific segments even if the overall result is null? - This could indicate the change is right for a subset of users. 5. Based on the diagnosis, recommend one of: - Re-run with larger sample size (provide new calculation) - Re-run targeting only the segment where effect appeared - Redesign the test with a stronger treatment - Accept the null — the change genuinely has no effect Return: power analysis, effect size assessment, duration check, segment analysis, and recommendation.
When to use this prompt
Use case 02
Use before launch to design an experiment or after launch to interpret results.
Use case 03
Use when you need to calculate sample size, validate significance, or diagnose weak tests.
Use case 04
Use when a decision depends on evidence rather than intuition or stakeholder opinion.
What the AI should return
The AI should return a decision-ready experiment output with the requested calculations, assumptions, and interpretation clearly labeled. Statistical reasoning should be explained in plain language, and the response should distinguish significance, practical impact, risks, and next steps. Any recommendation should be explicit, defensible, and tied to the evidence provided.
How to use this prompt
1
Open your data context
Load your dataset, notebook, or working environment so the AI can operate on the actual project context.
2
Copy the prompt text
Use the copy button above and paste the prompt into the AI assistant or prompt input area.
3
Review the output critically
Check whether the result matches your data, assumptions, and desired format before moving on.
4
Chain into the next prompt
Once you have the first result, continue deeper with related prompts in AB Testing and Experimentation.
Frequently asked questions
What does the Inconclusive Test Diagnosis prompt do?+
It gives you a structured ab testing and experimentation starting point for business analyst work and helps you move faster without starting from a blank page.
Who is this prompt for?+
It is designed for business analyst workflows and marked as intermediate, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
Inconclusive Test Diagnosis is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.
Can I use this outside MLJAR Studio?+
Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.
What should I open next?+
Natural next steps from here are A/B Test Design Brief, A/B Test Results Analysis, Experiment Roadmap Builder.