Lesson 3 of 12

Structured learning draft

Evidence with Datawrapper

In Data Journalism & Visual Storytelling, the way a learner handles evidence shapes how Datawrapper is used and evaluated. Policy evidence includes data, research, experience and implementation knowledge. This beginner lesson focuses on a decision or output that another person can inspect.

Learning objectives

Explain evidence in the context of Data Journalism & Visual Storytelling.
Apply Datawrapper to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureNested structure

Evidence: from context to evidence

Evidence connects public need and evidence to a accountable outcome in Data Journalism & Visual Storytelling.

Public need and evidence

Define the purpose, intended user and Datawrapper constraints.

frames

Evidence

Assess relevance, quality and uncertainty.

produces evidence for

Accountable outcome

Compare the observed result with a normal case, boundary case and stated limitation.

Takeaway: Evidence is credible only when the result can be traced back to its purpose, inputs and constraints. Evidence is the decision layer between the starting context and evidence that the result is fit for purpose.

Policy evidence includes data, research, experience and implementation knowledge. For Datawrapper, distinguish performing an operation from demonstrating that it suits the stated purpose. Assess relevance, quality and uncertainty. Record assumptions that could change the conclusion.

Apply evidence deliberately

State the Data Journalism & Visual Storytelling task and the decision it supports.
Prepare a small Datawrapper case with a known input and difficult boundary.
Assess relevance, quality and uncertainty.
Compare the observed result with the expected behaviour and explain differences.
Save the evidence, limitation and next action in a review record.

evidence evidence record
Review point	Evidence
Purpose	The specific Datawrapper outcome and intended user
Method	The evidence decision, input and version or context
Result	Observed output plus a checked boundary case
Limitation	What the result does not establish and the next safe action

Common mistakes

Using Datawrapper before defining what evidence must achieve.
Checking only the easiest Data Journalism & Visual Storytelling example.
Reporting a result without its input, assumptions or limitation.

Practice activity

Apply the lesson

For Data Journalism & Visual Storytelling, complete a bounded Datawrapper task demonstrating evidence. Keep the original input, numbered method, normal test, boundary test, observed results and a 100-word self-review naming one limitation and next improvement.

Check your understanding

In Data Journalism & Visual Storytelling, which evidence best supports a evidence result produced with Datawrapper?

A polished Datawrapper output without its input A screenshot showing the tool opened The input, expected behaviour, normal and boundary results, and a limitation A claim that the method always works

Lesson summary

For Data Journalism & Visual Storytelling, evidence means: Policy evidence includes data, research, experience and implementation knowledge.
A credible Datawrapper result includes a checked boundary, not only a successful example.
The next lesson builds on this evidence evidence record.

Sources and further reading

Open Government Data ToolkitWorld Bank - accessed 2026-08-21
Digital governmentOECD - accessed 2026-08-21

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