Skip to main content

High-impact AI in analysis, code and statistics

The guidance below highlights impactful applications of AI during analysis and testing, with practical considerations and prompt templates you can copy and paste to adapt to your own needs.

Aim: Move faster and safer by using AI to strengthen testing, not just generate code.

Concentrated african american woman with braids working in a science laboratory indoors; Shutterstock ID 2492753973; purchase_order: Bynder library; job: ; client: ; other:

High-impact use

  • Build a repeatable reproducible workflow others can rerun using structured starter code (code scaffolding) and change history.

  • Plan robustness checks such as sensitivity analyses and negative controls.

  • Ask AI to look for silent failure risks: information bleed (data leakage), too many tests (multiple testing), or the wrong analysis for the question (mis-specified model).

Safety check

  • Treat AI outputs as draft work, not final analysis: AI can sound confident while being wrong.

  • If your task involves calculations or data analysis, use an AI tool that can execute code (e.g., Python, R, or built-in data analysis mode), rather than one that only generates text.

  • Verification means rerunning analyses, testing code on known/simple cases, checking calculations on a subset manually, and confirming method assumptions.

  • For key analytical decisions, seek a peer-check, If that isn’t available, do two independent cross-checks (e.g., alternate method/tool; reproduce results from scratch on a subset).

  • Document settings, tool versions, datasets, and any AI-influenced analysis decisions.

Copy and paste prompts

📑 Copy and paste prompt: silent failure risk audit

Act as a statistical QA reviewer. Here is my planned analysis (high-level): [paste]

Identify ‘silent failure’ risks, including:

- data leakage / information bleed

- multiple testing / researcher degrees of freedom

- model mis-specification

- overfitting / data-driven feature selection

- inappropriate handling of missing data

For each risk: explain why it matters, how to detect it, and a concrete mitigation I can preregister.

⚠️ Important: This prompt generates generic risk categories relevant to your analysis type. It cannot detect actual leakage or mis-specification in your specific data/code. You must follow up by writing and running diagnostic code to test for each flagged risk in your actual data.

Explore all research stages