Use case 01
Use when a product, growth, or operations team wants to test a change rigorously.
Business AnalystAB Testing and ExperimentationIntermediateSingle prompt
Multiple Testing Correction 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 adjusts significance decisions when many metrics or variants were tested at once.
Prompt text
Apply multiple testing corrections to this experiment that tested multiple metrics or multiple variants simultaneously.
Test data provided includes {{num_metrics}} metrics and/or {{num_variants}} variants.
1. Explain the multiple testing problem:
- With {{num_tests}} independent tests at α=0.05, the probability of at least one false positive is {{familywise_error_rate}}%
- Without correction, we are likely to see spurious significant results
2. Apply and compare three correction methods:
a. Bonferroni correction: α_adjusted = 0.05 / number of tests
b. Holm-Bonferroni (step-down): less conservative than Bonferroni
c. Benjamini-Hochberg (FDR): controls false discovery rate at 5%
3. For each metric, show: raw p-value | Bonferroni adjusted | Holm adjusted | BH adjusted | significant after each correction?
4. Recommend which correction method to use for this specific test and why
5. Re-state the decision recommendation after applying the correction — does it change?
Return: corrected p-value table, method comparison, and final decision 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 Multiple Testing Correction 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?+
Multiple Testing Correction 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.