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Look a little closer · 3 minutes to explore

Check the story a chart tells

Look beyond an attractive chart and test the explanation beside it. Use a fictional dataset to distinguish counts, percentages, and causal claims before repeating the story in a report.

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Shape your example

Fictional support data: 8 of 10 requests were completed in week one and 12 of 20 in week two. Staffing and request difficulty also changed.

Claim 1More requests were completed in week two.

Sample evidenceCompleted counts rose from 8 to 12.

Claim 2The completion rate improved in week two.

Sample evidenceThe rates are 8/10 = 80% and 12/20 = 60%.

Claim 3The new checklist caused the change in completions.

Sample evidenceThere is no controlled comparison, and staffing and request difficulty changed.

Something you can use

Separate claims from evidence

Fictional support data: 8 of 10 requests were completed in week one and 12 of 20 in week two. Staffing and request difficulty also changed. Classify what the displayed sample supports, then inspect the explanation.

Checked
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Matches the sample
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These are authored sample claims and evidence. Unsupported here means not established by this evidence, not necessarily false in the world.

A little recognition

For the moments like this.

A presentation shows a rising line and says that a recent change improved performance. You want to understand whether the data supports that conclusion or merely shows two things happening together. Inspect a small fictional set of counts and conditions, then decide which claims can safely appear in the accompanying explanation.

What becomes possible

AI can help propose chart explanations and reveal questions about denominators, missing observations, and comparisons. Here you classify three statements using visible sample counts rather than trusting the chart's confident caption. This sample asks you to judge three claims against visible authored evidence. The explanations are prepared examples, not external facts or live AI verification.

A possibility becomes yours when you use it

Take the next little step.

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A little help, right here.

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Sources, capability notes and how this example works

This local experience uses authored sample content and deterministic controls. Changing an input updates the example; no model runs, outside records are checked, or real-world actions are performed. Use the sources for the underlying AI capabilities and audience context, and the human check above when you apply the idea.

Data analysis with ChatGPT ↗Current capability documentation; analysis depends on the supplied records and needs review. Accessed September 11, 2026.

Research checked September 11, 2026. Access to specific AI features depends on your account, plan and region. Sample amounts, timings, scores and feedback illustrate the method rather than claim measured outcomes.

Keep a little curiosity

Another useful possibility.

All ten in this path ↗