Key concepts: AI in data analysis
Know these before you put a bot to work in data analysis. They are why the techniques and prompts are built the way they are.
Schema, not data
You can usually share table definitions without sharing rows.
Correlation vs causation
Bots happily write causal stories; challenge them.
Reproducibility
Save the code, not just the answer.
Privacy
Personal data rules (e.g., state privacy laws, HIPAA, FERPA) still apply.
How chatbots fail
Language models predict likely text. They can produce fluent, confident answers that are wrong, out of date, or that cite sources that do not exist. They also repeat errors in what you paste in. Ask for sources, ask the bot to mark uncertainty, and check everything that matters.
Accuracy and ethics, every time
- Confidentiality: never paste client, patient, student, employee or customer personal data (PII/PHI) into a public bot. Use tools your organization has approved, with contract terms that cover data use, or remove identifiers first.
- Verify everything: bots can invent citations, case law, statistics, doses, code sections and quotes that look real. Check each one against the original source before you rely on it.
- Not professional advice: these tutorials teach you how to use AI tools. They are not legal, medical, tax, financial or engineering advice.
- A human professional is responsible: the licensed or accountable person reviews, decides and signs. The bot drafts; you own the result.
- Prices change: we don't list product prices. Check each vendor's official pricing page.
Official sources for this section
- NIST AI Risk Management Framework: AI risk
- NIST/SEMATECH e-Handbook of Statistical Methods: free statistics reference
- Data.gov: public datasets for practice
Links checked Oct 30, 2026. Rules differ by state and country; follow your licensing body and employer policy.
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