Interpret a table of data without overclaiming
Reads a table or CSV, answers your question with the numbers, and flags what the data can and can not tell you.
Here is some data:
```
{{DATA}}
```
Question: {{QUESTION}}
1. Describe what the data contains: columns, rows, time range, any obvious gaps.
2. Answer the question, showing the numbers and calculations step by step.
3. Say how confident we can be: sample size, time period, outliers, missing data, and whether the result could be chance.
4. Name other explanations for the pattern.
5. Suggest the next analysis that would give a firmer answer.
Do not claim causation unless the data comes from a controlled experiment.Example output
Answer: conversion rose from 2.1% to 2.6% after the change. Caution: only 9 days of data and 380 conversions; the change could be noise. Next check: run 3 more weeks or compare with the same period last year.
How to use
Paste CSV or a copied spreadsheet range with headers. For anything beyond a few hundred rows, use a model with code execution so it calculates instead of estimating.
Why it works
Showing calculations step by step reduces arithmetic errors and lets you check the work. The confidence and alternative explanations sections stop a single number from turning into a story it can not support.