1. Descriptive Analytics

Visual Evidence and Data Stories

Choose displays for comparison, uncertainty and action rather than decoration

Visual Evidence and Data Stories

Begin with the reader’s task

TaskUsually effectiveCommon failure
compare categoriessorted dot or bar plot3D perspective and area distortion
trace timeline plot with event annotationstruncated period or dual axes
inspect distributionhistogram, ECDF or box/violin plus pointsmean-only bar
inspect relationshipscatterplot with groups and uncertaintyoverplotting or fitted line without raw data
compare actual with targetaligned point/interval displaytraffic-light colour without scale

Position on a common scale is generally decoded more accurately than angle or area. Cleveland and McGill’s graphical-perception experiments provide the foundation; the lesson is to match encoding to comparison, not to ban one chart mechanically.

One display, one decision question

  • Weak title: Delivery performance
  • Decision title: Evening slots account for 71% of delays above 30 minutes; test capacity before changing all promises

The second title states the pattern and the proposed next step. It must still show:

  • period and population;
  • denominator and sample size;
  • comparison or baseline;
  • uncertainty or data limitation;
  • annotation for operational changes.

Do not hide the scale

A bar from 48% to 52% can make a four-point change fill the page. For magnitude comparisons, bars normally begin at zero. A line chart may use a narrower axis to reveal change, but label the range and provide context.

Dual axes can imply a relation by choosing convenient scales. Prefer aligned panels or index both series to a documented baseline when the comparison is about relative change.

Display uncertainty where action depends on it

Suppose Store A late rate is 8% from 100 orders and Store B is 7% from 10,000. The one-point ranking hides radically different precision.

Show:

  • interval estimates or posterior distributions;
  • sample size and exposure;
  • forecast bands across horizon;
  • scenario ranges for assumptions;
  • missing-data and revision indicators.

Uncertainty is not a decorative haze. It should help the reader decide whether alternatives are meaningfully different. Research on uncertainty-visualisation evaluation also warns that visual accuracy is not the same as decision quality.

Accessibility and integrity checklist

  • title states the question or conclusion;
  • text and table provide the key values;
  • colour is redundant with labels, shape or line style;
  • palette remains distinguishable with colour-vision differences;
  • labels use units and plain language;
  • source, cutoff and exclusions are visible;
  • missing periods are gaps, not connected lines;
  • annotations distinguish observed events from interpretation.

A three-sentence data story

  1. Context: “Across 24 completed weeks, 7.8% of accepted deliveries were late.”
  2. Evidence: “The rate was 4.1% before 17:00 and 13.6% after 17:00; evening orders also had larger baskets and longer routes.”
  3. Actionable uncertainty: “Pilot one extra picker in two comparable evening zones; the aggregate split alone does not identify a staffing effect.”

The story does not pretend the chart has already answered the causal question.

Quick check

A chart highlights the top five stores by complaint count. What essential exposure may be missing?

Answer
Order or customer volume. Show complaint rate as well as count, and keep the count because a low-rate high-volume store may still create the most total harm.

Next: Experiments and Causal Boundaries

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