Skill · Version 1.0.0

Choose honest charts

Chooses visual forms and scales that make the intended comparison understandable without distorting the meaning of the data.

chart-selection-visual-encoding

See how it helps

When is it useful?

Use when a chart must answer a specific question without misleading comparisons.

Matches trends, categories, distributions and correlations to suitable chart types.
Uses aligned positions and clear baselines for meaningful quantitative comparisons.
Avoids distorted areas, inconsistent scales and misleading dual axes.

Redesign a revenue-versus-refund chart so differently scaled axes cannot imply a false relationship.

Read the skill’s original instructions, examples and checks in their authored language.

Details…Show less

Use this skill when choosing chart types, mapping data attributes to visual channels, and designing scales, axes, and legends.

Match the Chart to the Analytical Task and Data Type:
- Trend & rate of change over ordered time: Line chart, step chart (for discrete state/price changes), or horizon/area chart.
- Magnitude comparison across categories: Horizontal or vertical bar chart, Cleveland dot plot.
- Distribution & outliers: Histogram, box/violin plot, strip/beeswarm plot, or quantile band (never hide a bimodal distribution behind a single mean bar).
- Part-to-whole composition: Stacked bar (for a few segments), 100% stacked bar, marimekko, or treemap (avoid multi-slice pie/donut charts when $>3\text{–}4$ categories or close values must be compared).
- Correlation & joint variation: Scatter plot, hexbin/contour density plot, parallel coordinates, or heatmap.
Respect Cleveland & McGill's Perceptual Hierarchy:
- Encode primary quantitative comparisons using position along a common aligned scale first, then length, before falling back to area, angle, or color saturation.
- Never use 3D extrusions, perspective distortion, or decorative gauges that falsify perceived proportions.

Mandatory Zero Baseline for Length/Area Encodings:
- Bar and filled area charts encode magnitude by length/area and must start at 0. If a narrow non-zero range is needed to inspect small variations, use a line or dot plot with clearly labeled axis bounds.
Avoid Deceptive Dual Y-Axes:
- Plotting two metrics on independent left/right Y-axes lets arbitrary scale stretching manufacture fake visual correlation. Prefer indexed small multiples (% change) or vertically stacked linked panels sharing an X-axis.
Consistent Scales Across Small Multiples:
- Lock side-by-side comparison panels to a shared scale by default; if independent scaling is toggled, mark the axis warning unmistakably.
Explicit Non-Linear Scales:
- Use log scales only across multiple orders of magnitude or multiplicative growth rates, and label ticks and axis titles clearly (log10 scale).

Does the chosen chart type directly answer the user's comparison, trend, distribution, or composition question?
Do all bar and area charts start at a 0 baseline without arbitrary axis truncation?
Have dual-axis distortions and 3D/area misproportions been avoided in favor of aligned common scales or linked panels?
Are units, tick formatting, scale types (linear vs. log), and legends unambiguous?