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Audit a chart for misleading design

Autor: AILesson7 min de configuraçãoTestado com:ChatGPTRevisado em: 2026-08-28

Resposta rápida

Review scale, encoding, labels, missing context, and the claim against source values. Forneça: Chart description, Source values and definitions, Audience and intended claim. Resultado esperado: A severity-ranked chart audit and a corrected design specification.

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Seu prompt

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Audit this chart for misleading or unclear design.

Chart type, axes, scales, labels, colors, annotations, and title:
[chart]

Source values, definitions, units, denominators, periods, and missing data:
[source]

Audience and intended claim:
[claim]

Compare every visual encoding and textual claim with the source. Check truncated or unequal axes, area/volume distortion, dual axes, category order, omitted baselines, cherry-picked periods, changing denominators, missing uncertainty or sample size, color accessibility, and title wording. Rank findings as blocking, material, or minor. Then specify a corrected chart and state the strongest claim the evidence actually supports. Do not invent unseen source details.
Experimente no Playground
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Da entrada ao resultado

Um exemplo prático

Veja como um contexto concreto transforma esta receita em um resultado útil

Entrada real

Chart description
A 3D column chart titled 'New training doubles productivity'. It shows Before=82 and After=91 tasks per week. The y-axis begins at 80. Before is gray and After bright green. No sample size or dates are shown.
Source values and definitions
Same 12 employees measured for four weeks before and four weeks after training. Values are group means. No control group. Two employees were on leave during one after week. Individual variation and uncertainty are not supplied.
Audience and intended claim
Management slide intended to justify rolling the training out company-wide.

Exemplo de saída

Blocking findings

The title is false: 91 is 11.0% above 82, not double. The y-axis starting at 80 makes a nine-task increase occupy almost the full chart height, and 3D perspective adds further visual distortion. Without a control group, the before-after difference cannot be attributed to training.

Material findings

The chart omits n=12, the four-week windows, paired measurement, leave-related coverage difference, and uncertainty. A mean alone hides whether improvement was widespread or driven by a few employees. Bright green also implies approval rather than neutral comparison.

Corrected specification

Use a paired dot plot of each employee's before and after mean if individual data is available, plus a clearly labeled group summary. Otherwise use a simple two-point slope or column chart with a zero baseline, flat 2D design, exact labels 82 and 91, and subtitle “Observed mean increased by 9 tasks/week (11%); n=12; uncontrolled before-after comparison.” Add the date windows, coverage note, and uncertainty once calculated. The strongest supported claim is that the observed mean was higher after training; the data does not establish that training caused the increase or predict company-wide results.

Por que isso funciona

  1. 1

    Checking the visual against source values catches distortion that a style-only review misses.

  2. 2

    Severity ranking separates truth-changing defects from ordinary polish.

Verifique o resultado

  • Are visual distances proportional to the underlying values?

  • Does the title describe evidence rather than imply unsupported causality?

  • Are denominators, periods, exclusions, and sample sizes visible where needed?

Evidências por trás deste método

Fontes e citações

Fontes primárias que apoiam escolhas específicas de design nesta receita. Elas não garantem um resultado específico de IA.

  1. The audit checks chart clutter, readable labels, text alternatives, and linked data sources because official UK guidance identifies these as core chart accessibility and transparency practices.

    Accessible charts: a checklist of the basics — UK Government Analysis FunctionFonte revisada em:

Use com confiança

Perguntas frequentes

Respostas práticas sobre quando usar esta receita, o que fornecer e onde a revisão humana ainda é importante

What should I prepare before using “Audit a chart for misleading design”?

For “Audit a chart for misleading design,” prepare Chart description, Source values and definitions, and Audience and intended claim. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.

When is the “Audit a chart for misleading design” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—A severity-ranked chart audit and a corrected design specification—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.

Which AI tools have recorded tests for “Audit a chart for misleading design”?

The published test record for “Audit a chart for misleading design” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.

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Onde esta receita se encaixa

Prompts de textoAnálise de dadosFeedback e revisãoExcel e planilhas

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