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Look a little closer · 3 minutes to explore

Find the problem behind the total

Inspect a small worksheet problem before trusting its total. Follow the effect of a duplicate record, distinguish a likely issue from a verified correction, and keep the original evidence visible.

An original hands-on example. Free to explore. This example runs in your browser; the optional AI guide below is separate.

Bring an idea. Shape it. Use what helps.

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Shape your example

Fictional export: record A17 appears twice at $40 each; B22 appears once at $60. The rule is to count each record ID once.

Claim 1The unique-record total is $100.

Sample evidenceA17 contributes $40 once and B22 contributes $60 once under the stated rule.

Claim 2The export system always creates duplicates.

Sample evidenceOnly one small export has been inspected.

Claim 3The correct unique-record total is $140.

Sample evidenceThat total counts both appearances of A17 despite the one-per-ID rule.

Something you can use

Separate claims from evidence

Fictional export: record A17 appears twice at $40 each; B22 appears once at $60. The rule is to count each record ID once. Classify what the displayed sample supports, then inspect the explanation.

Checked
0 of 3
Matches the sample
0 of 3

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 familiar spreadsheet total suddenly looks too high. You suspect a duplicate, but deleting a similar-looking row could remove legitimate work. The fictional sample gives you record identifiers, amounts, and a stated counting rule. Use those details to judge the explanation before deciding what a responsible correction would require.

What becomes possible

AI can help inspect a supplied worksheet and suggest checks for duplicates, inconsistent types, or unexpected totals. Here you test three claims about a small fictional export against its explicit counting rule. 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.

Your choices from the experience travel into a practical starter. Shape it in your own ChatGPT, or explore personal help with Kevin.

Continue in your ChatGPT.

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A little help, right here.

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The guide helps you explore and apply an idea. Your direction, decisions and approval remain yours.

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 ↗