Pressure-Test Any AI Analysis: How to Tell If It's Right

How to pressure-test an AI analysis and know whether it is actually right, even when you have no answer key to check against. AI analytics tools hand you a confident answer whether it is correct or not, and a wrong number looks exactly like a right one. This session is a repeatable way to check it: reach for a trusted reference when you can, and bound the risk when you cannot. You will see the four no-key checks (reliability, receipts, reconciliation, and reading the trace), where each one falls short, and when to act, dig, or abstain. Then a live demo in Claude Code catches a revenue breakdown that claimed $6.9M when the real number was $3.7M, an 87% overstatement, before it could reach a board deck. What you will learn: Find or forge a reference to check an AI analysis against Handle the novel question that has no answer key, with checks you can run on any output Know what each check actually catches, and where it falsely passes Decide when to act, when to dig, and when to abstain Chapters: 0:00 A confident wrong number 0:14 Intros and weekly free workshops 2:23 Why a wrong answer looks right 3:36 Poll: how do you check today? 9:25 Recap: the reliability check (retention 9-99%) 12:46 Analysis is a pipeline that can fail 14:19 Ground truth and finding a reference 19:18 Situation B: the four no-key checks 24:07 Where each check falls short 27:02 Act, dig, or abstain 30:43 Live demo: catching an 87% error 41:24 Close, resources, and Q&A A free Lightning Lesson from AI Analyst Lab. More at https://maven.com/dataneighbor #AIAnalytics #DataAnalysis #AIEvals