How it works
Visualizations can reveal patterns, but design choices can also mislead readers.
Choose the visualization that fits the relationship: comparison, trend over time, distribution, or association.
Common error
Correlation is not causation; a chart can show a pattern without proving why it happened.
Worked example
Read a small time series and limit the claim to what was measured.
Data / pseudocode
Week 1: 20 visits
Week 2: 25 visits
Week 3: 40 visits
New poster displayed during week 3A line chart can show the change over time. Visits rose by 20 from week 1 to week 3, a 100% increase relative to week 1. The timing alone does not prove the poster caused the increase; other events or measurement changes may matter.
Exercises
2.3.1
Which visualization fits comparing total visits across four clubs, and which fits visits over ten weeks?
Show solution
A bar chart fits comparing club categories. A line chart fits the ordered time trend. Label units and use an appropriate scale.
2.3.2
An app chart shows that more study time is associated with higher scores. State a defensible claim and an unsupported causal claim.
Show solution
Defensible: the measured data show an association between study time and score. Unsupported: changing study time alone necessarily caused the score difference. Other factors and study design affect causal conclusions.