Topics
AI data analysis prompts for trends, outliers, segments, surveys, forecasts, charts, and executive summaries with explicit definitions and checks
A polished analysis can still be wrong when metrics, denominators, missing values, or time periods are unclear. These prompts start from the data contract and require reconciliation before interpretation.
Structure assignment, exposure, outcomes, exclusions, uncertainty, and decision rules without overstating a test
Connect every dashboard number to a decision, population, formula, source, owner, refresh rule, and limitation
Compare groups using consistent metrics, denominators, coverage, and minimum sample rules
Match the summary statistic to the decision, metric scale, distribution, weighting, and tail risk
Match the comparison task and data shape to a chart without distorting scale or uncertainty
Normalize price, quantity, quality, fees, conditions, timing, rewards, returns, and unused volume before comparing savings
Compare complete periods with explicit baselines, denominators, and missing-period rules
Review scale, encoding, labels, missing context, and the claim against source values
Separate sustained change, seasonality, and one-off noise using explicit time periods
Combine rating distributions with traceable comment themes and response-bias limits
Measure missingness by field and group before deciding whether to leave, recover, or exclude it
