Infographics have become a standard tool for sharing research findings. They promise quick understanding, visual appeal, and audience engagement. However, glossy design alone does not guarantee persuasive communication. In many cases, “pretty ≠ persuasive.”
Research communication works only when visuals provide clarity, causality, and credibility—not gradients, 3D bars, or rainbow color palettes.

Below is a deeper look at the major failures of glossy infographics and how evidence-based design can transform research outputs into decision-grade communication.

Pretty ≠ Persuasive: Why Style Alone Fails

Most visually appealing infographics do not help readers make better decisions. A 2022 analysis by Evergreen (Evergreen Data) notes that over 70% of public-facing research infographics contain misleading visual elements. Good visuals must act as extensions of the research process—not decorations.

Glossy design often hides:

• What the audience should conclude
• How strong the evidence is
• What the data cannot prove
• What decision the reader should make next

In research, clarity outranks aesthetics.

Major Gaps That Reduce Research Impact

1. No Claim Hierarchy

Many infographics present too many facts without identifying:

• The primary takeaway
• Supporting evidence
• Secondary insights
• Necessary caveats

Fix it:

• Add a headline takeaway at the top.
• Support it with evidence, numbers, and citations.
• Use micro-headings to guide the eye.

2. Correlation Posing as Causation

One of the most frequent issues is failing to distinguish:

• Correlation (variables move together)
• Causation (one variable produces a change in another)

Readers often assume causality unless explicitly corrected.

Fix it:

• State the mechanism or note if unknown.
• Add design cues like “correlation only” tags.
• Mention the study design (e.g., RCT, survey, longitudinal).

3. Hidden Effect Sizes

Infographics often highlight percentage increases without context.

Example:
“Risk increases by 50%” means little without baseline risk.

Hiding effect sizes makes visuals persuasive but not honest.

Fix it:

• Show baseline values
• Show absolute change
• Add confidence intervals (CIs)
• Use reference lines to anchor understanding

4. Distorting Visuals

Common visual manipulations include:

• 3D charts
• Dual axes
• Rainbow color maps
• Unlabeled scales
• Oversized icons

These distort perception of magnitude and direction.

Fix it:

• Use 2D charts only
• Stick to 1 axis per chart
• Use color with purpose, not decoration
• Maintain consistent scales

5. No Uncertainty or Accessibility

Research is probabilistic, yet infographics often present results as absolute.

Fix it:

• Add error bars, CIs, or uncertainty intervals
• Use high-contrast fonts
• Include alt text for accessibility
• Avoid small or decorative typefaces

6. Not Decision-Ready

Most research visuals end with a “so what?” moment.

Decision-makers need:

• What this result means
• What action is recommended
• What comes next

Fix it:

End with a clear “What this means” or “Recommended next step” section.

How Research & Report Consulting Improves Visual Impact

A high-quality research infographic requires a storyboard → audit → publication workflow:

Storyboard

• Define audience role
• Identify decision points
• Select the core argument

Audit

• Remove distortions
• Reveal effect sizes
• Add uncertainty
• Fix color/contrast

Publication

• Produce accessible, journal-grade visuals
• Provide alt text, data notes, and citations

As a result, the research becomes:

• Credible
• Actionable
• Decision-ready

References

1. Evergreen, S. Effective Data Visualization.
2. Tufte, E. The Visual Display of Quantitative Information.
3. NIH: Principles of Effective Data Visualization.
4. DataViz Catalog.
5. Harvard Data Science Review.

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