Creating charts seems straightforward until you realize your bar graph is confusing, your pie chart is misleading, or your colors are causing eye strain. Even experienced professionals make chart mistakes that undermine their message and credibility.
This guide explores the 10 most common charting mistakes and shows you exactly how to avoid them, drawing on principles from Edward Tufte's The Visual Display of Quantitative Information and Stephen Few's Show Me the Numbers.
1. Using 3D Effects That Distort Data
The Problem: Adding 3D effects to bar charts, pie charts, or column charts makes them "pop" but distorts proportions and makes accurate comparisons impossible. A 40% slice can appear larger than a 50% slice depending on the viewing angle.
How to Fix It: Stick to 2D charts. If you need visual interest, use color gradients, subtle shadows, or icons instead.
Example: Consider a sales report showing market share with a 3D pie chart. The perspective angle can make a 10% market share slice appear equal to a 25% slice, leading to misallocated resources and poor strategic decisions. This distortion effect has been documented in numerous data visualization studies showing that 3D charts consistently reduce comprehension accuracy.
2. Choosing the Wrong Chart Type for Your Data
The Problem: Using pie charts for trends over time, line charts for unrelated categories, or bar charts for correlations confuses readers because each chart type is designed for specific data relationships.
Quick Reference Guide:
- Trends over time? → Line charts or area charts
- Comparing categories? → Bar or column charts
- Part-to-whole (max 6 parts)? → Pie or donut charts
- Correlation between variables? → Scatter plots
- Distribution? → Histograms or box plots
Example: Imagine a marketing dashboard using a pie chart to show website traffic across 12 months. The growth trend that would be immediately obvious in a line chart is completely hidden, making it impossible to identify seasonal patterns. When the same data is displayed as a line chart, month-over-month trends and seasonality become instantly apparent.
Line charts are perfect for showing trends over time
3. The Rainbow Effect: Using Too Many Colors
The Problem: Assigning different colors to every data point creates visual overwhelm. The human eye struggles to track more than 5-7 distinct colors in a single chart.
Color Strategy:
- Use 2-4 colors maximum
- Reserve bright/bold colors for the most important data
- Use shades of one color for related categories
- Keep secondary data in neutral grays
- Ensure sufficient contrast (WCAG 2.1 requires 4.5:1 ratio)
Example: A quarterly performance dashboard displaying 15 different colors for 15 product lines creates cognitive overload. After redesign using 3 categories (exceeding/meeting/below target) with neutral shades for individual products, executives can immediately identify performance issues without having to decode a rainbow legend.
4. Truncating Y-Axis to Exaggerate Differences
The Problem: Starting a bar chart's Y-axis at 50 instead of 0 to make a change from 60 to 65 look dramatic is one of the most common ways to lie with statistics.
The Rule: Always start bar and column chart Y-axes at zero. For line charts showing trends, you have more flexibility, but be transparent. If truncating is necessary, add a clear axis break symbol.
Example: In their book Calling Bullshit (2020), Bergstrom and West analyze corporate earnings presentations and find that truncated Y-axes are frequently used to exaggerate modest growth. A Y-axis starting at 95 can make revenue growth from 98M to 100M appear as a massive spike rather than the 2% increase it actually represents.
Always start bar and column chart Y-axes at zero for accurate comparisons
5. Cluttering Charts with Unnecessary Elements
The Problem: Adding gridlines, data labels, legends, titles, subtitles, borders, shadows, and background images all at once increases cognitive load and distracts from data.
Apply Tufte's Data-Ink Ratio: Maximize the proportion of ink dedicated to data versus decoration.
- Remove or lighten gridlines (use white or light gray at 10-20% opacity)
- Use direct labels instead of legends when possible
- Eliminate borders and background fills
- Remove redundant axis labels
Example: Professional financial reports from firms like BlackRock demonstrate minimalist principles—sparse gridlines, direct labeling, and zero decorative elements. This approach allows readers to focus on data insights rather than visual noise.
6. Using Inconsistent Scales Across Related Charts
The Problem: Creating multiple charts in a dashboard where each has different Y-axis scales makes visual comparison impossible. A small bar in one chart might represent more value than a tall bar in another.
When to Use Consistent Scales:
- Comparing performance across regions, products, or time periods
- Dashboard views where users scan multiple charts
- Before/after comparisons
Example: The CDC's COVID-19 data dashboards initially showed state-by-state case counts with auto-scaled Y-axes, making visual comparison impossible. After user feedback, they switched to consistent scales across states, allowing users to immediately identify hotspots through visual height comparison.
7. Creating Pie Charts with Too Many Slices
The Problem: Humans are poor at comparing angles and areas. Cleveland and McGill's seminal research (1984) found that bar charts are 20-30% more accurate than pie charts for comparison tasks. Beyond 5-6 slices, pie charts become unreadable.
Pie Chart Rules:
- Limit to 5-6 slices maximum
- Combine small categories into "Other"
- Sort slices by size (largest at 12 o'clock, descending clockwise)
- Consider a sorted bar chart instead
Example: Gartner's research reports on market share avoid multi-slice pie charts, instead using horizontal bar charts sorted by size. This makes competitive positioning immediately clear and allows readers to compare precise differences between competitors.
Limit pie charts to 5-6 slices maximum for readability
8. Ignoring Colorblind Accessibility
The Problem: Red-green, blue-purple, and certain other color combinations are indistinguishable to approximately 8% of men and 0.5% of women with color vision deficiency.
Accessible Design:
- Avoid red-green combinations
- Use patterns, textures, or shapes in addition to color
- Test with colorblind simulation tools (Coblis, Color Oracle)
- Use colorblind-safe palettes (blue-orange-red combinations)
- Ensure sufficient contrast
Example: Tableau updated their default color palette to colorblind-safe combinations after research showed that a significant portion of dashboard users couldn't distinguish their previous red-green scheme. The accessible palette maintained visual appeal while ensuring universal readability across all types of color vision.
9. Missing or Misleading Labels
The Problem: Charts without axis labels, units, dates, or data sources leave viewers confused about what they're looking at.
Essential Labels:
- Axis titles with units (dollars, thousands, percentages)
- Time periods clearly specified
- Currencies and magnitudes (M for millions, K for thousands)
- Data source citations
- Descriptive titles that explain the insight, not just describe the data
Example: Instead of a title like "Revenue," use "Q4 Revenue Grew 25% to $625K, Driven by Product A." This follows best practices from publications like the Wall Street Journal—tell readers what to see, don't make them figure it out.
10. Using Default Chart Styles Without Customization
The Problem: Excel, Google Sheets, and other tool defaults often include 3D effects, bright gradients, and awkward spacing. Default styles signal lack of effort and rarely align with professional or brand standards.
Customization Checklist:
- Match colors to your brand or use professional palettes
- Remove 3D effects and gradients
- Adjust fonts to match your document style
- Remove unnecessary gridlines and borders
- Export at proper resolution (300 DPI for print, 2x pixel density for web)
- Create and save custom templates for consistency
Example: Major publications like The Financial Times, The Economist, and FiveThirtyEight all use consistent custom chart styles that are immediately recognizable. Their publicly available style guides demonstrate the power of professional, branded visualization in building reader trust.
Conclusion
Even small charting mistakes can undermine credibility and obscure important insights. By avoiding these 10 common errors, you'll create visualizations that are accurate, professional, and genuinely helpful to your audience.
The best chart is one where the viewer immediately understands your message without confusion or misinterpretation. Keep it simple, keep it honest, and always prioritize clarity over decoration.
Next Steps: Audit your existing charts against this list. You might be surprised how many quick fixes can dramatically improve your data storytelling.
Try the free chart tools at 5of10.com to create professional visualizations that avoid these common mistakes.