AI-Assisted Data Analysis

This bundle is for anyone who has uploaded a spreadsheet or a data file to an AI chat tool and asked it to analyze the file, without a data science background and without knowing whether the answer that came back was a real finding or a well-formatted guess. It does not require statistics training. It requires knowing the specific ways this workflow succeeds and the specific ways it silently fails, since both look identical from the outside.

Start with What AI Is Actually Doing With Your Data for the central spectrum the rest of the bundle builds on, then work through the three sections in order: getting a full dataset in and understanding what a given tool actually guarantees about it, asking analytical questions that produce a defensible answer while catching the specific documented ways AI gets numbers wrong, and — if you are going to run this kind of analysis again and again — turning a one-off result into a method you can actually rely on.

In this section

Page	Last updated
What AI Is Actually Doing With Your Data The mental model this bundle builds on: AI can be a genuinely capable analyst or a confident fabricator of numbers, depending entirely on how you use it.	Updated 2026-07-06
Getting Your Data In Why dropping a file into a chat window and saying 'analyze this' silently fails more often than people realize, and what different tools actually guarantee once the data is in.	Updated 2026-07-06
Doing the Analysis Once your data is properly in, how to ask questions that produce a real, defensible answer, and the specific, documented ways AI gets numbers wrong even when it appears to be doing real work.	Updated 2026-07-06
Making It Repeatable Turning a result you got once into a method that holds up on the next dataset, not a lucky prompt.	Updated 2026-07-06
Glossary Plain-language definitions of the terms used across this bundle.	Updated 2026-07-06
Validation How this bundle is validated — sourced claims are checked and each sweep is recorded here.	Updated 2026-07-06