Analytical Integrity and Ethical Decision Making

What it is Analytical integrity means doing rigorous, honest work with data: choosing sound methods, resisting p‑hacking, reporting uncertainty, and not cherry‑picking results. Ethical decision making adds a lens of stakeholder harms, fairness, privacy, and regulatory obligations when collecting data, building models, and shipping features.

Why interviewers ask about it Data scientists influence ranking systems, ads delivery, integrity tooling, and safety filters—errors can misallocate billions in spend or harm users at scale. Teams want people who can push back on bad metrics, detect leakage, surface subgroup harms, and navigate requirements like risk management and documentation rather than “making the graph go up” at any cost.

Core ideas to know

A common pitfall Candidates stay abstract (“be ethical”) instead of naming concrete controls. For example, they can’t explain how they’d prevent p‑hacking in a growth A/B test, or how they’d detect that a churn model used future information. Others ignore subgroup analysis, so they miss that a ranking change boosts overall CTR but depresses creator reach for small markets—a classic Simpson’s paradox. Strong answers pair principles with tactics, trade-offs, and a stop-ship threshold.

Further reading