Prompting AI for Data Analysis

Start with the decision or question you want the data to answer. Describe the dataset’s columns, units, date range, collection method, and known limitations; stored values alone may not reveal what a field means. Specify the groups or periods to compare, the output you need, and any rules for missing or duplicate records. If the method depends on assumptions, ask the assistant to state its proposed approach and why it fits before interpreting the result. This gives a person a chance to correct a mistaken premise early. AI tools differ in how they handle files and calculations. OpenAI’s current ChatGPT data-analysis documentation describes uploading data, viewing it in an interactive table, asking natural-language questions, comparing variables, and requesting charts or statistical analyses. Availability and exact behavior depend on the product and workspace, so check the tool you are using. A friendly chart or precise-sounding summary is not evidence that the right rows, units, joins, or definitions were used. Ask for calculation steps or code when the interface provides them, and verify filters, aggregation, and denominators against the original file. Check important values independently with a spreadsheet formula, a second calculation, or a reviewed analysis script. Inspect missingness and duplicates rather than assuming they were handled as intended. If a question is causal, consequential, or requires a specialized method, involve someone qualified to validate the design and interpretation. Protect confidential data: use only tools and workspaces approved for the dataset, and remove unnecessary identifiers. AI can help explore and explain a dataset, but a human remains responsible for context, assumptions, and decisions made from the result.