See it work

The chart is the destination. The conversation is the difference.

A real exchange with the generator — synthetic data, five schools, eight quarters, and one question: did our attendance intervention at Lincoln HS actually work?

It asks before it assumes — starting with privacy

The first thing it did with this file: confirm there's no student-level data, read the structure back in plain language, and ask what question the chart should answer — track the district over time, or compare schools?

Chat exchange: the generator confirms the upload contains only school names and counts with no student-level identifiers, describes the five-school quarterly dataset, and asks whether the goal is district trends over time or comparing schools.

It proposes a plan — and confirms before drawing

It heard “did the intervention work,” translated it into a before/after chart with the split placed where the intervention started, explained why — and asked for a go-ahead before building anything.

Chat exchange: the generator proposes a p-chart of Lincoln HS chronic absence with a phase split at the October 2025 intervention, asks whether that sounds right, and offers buttons to confirm or choose another path.

It answers the question — and says what the chart can't prove

The verdict: a real, sustained shift — chronic absence dropped from about 30% to 22% and stayed there. And the honest caveat: a chart shows that something changed when the intervention started, not that the intervention caused it. Then it offers to rule out a district-wide trend.

A phased p-chart titled Lincoln HS — Chronic Absence Rate showing the center dropping from 30.1% to 22.3% after the attendance intervention, followed by the generator's interpretation with the caveat that the chart can't prove the intervention caused the change.

Ready when you are

Your data is ready to teach you something.

Bring a spreadsheet and a question. The generator handles the statistics.