DGB 3: Data, Cause and Effect, and Predictions

← Digital Literacy – Year 3

Data can strengthen explanations and arguments, but it can also be overinterpreted. A central skill is distinguishing an observed relationship from proof that one factor caused another and presenting evidence fairly enough for others to evaluate it.

Curriculum reference

Area	Classification
Competence area	Information
Perspectives	T, G, I
Focus	Using data to present cause-and-effect relationships, predictions and ideas
Competence	Use data to support statements about possible cause-and-effect relationships and predictions and to communicate ideas without confusing correlation with causation.

Learning objectives

Students can:

select data that is relevant to a question or claim;
use tables and charts to communicate a data-supported idea;
distinguish correlation from causation;
formulate possible cause-and-effect relationships cautiously;
derive predictions from data while stating assumptions;
identify missing variables and alternative explanations;
recognise when a visualisation or selected data creates a misleading impression;
communicate conclusions together with limitations.

Learning content

Data as a basis for statements

A data-supported statement should show where the data comes from, what was measured and how the result relates to the claim. Relevant data is more important than a large quantity of unrelated numbers.

Showing possible cause and effect

If two values change together, one may influence the other, both may be influenced by a third factor, or the relationship may be coincidental. Data can suggest causal questions but does not automatically prove causation.

Correlation versus causation

The distinction is essential: correlation describes a relationship in observations; causation claims that a change in one factor contributes to a change in another. Strong causal claims require additional evidence and suitable study design.

Using data for predictions

Predictions use observed patterns and assumptions about future conditions. The prediction should state the time range, relevant conditions and sources of uncertainty.

Using data to communicate an idea

A well-chosen graph or table can make an argument understandable. Labels, source information and fair scales help audiences evaluate the message instead of merely being persuaded by appearance.

Selecting and organising data

Choose variables, time periods and categories before producing a chart. Selective omission can distort a conclusion even when every individual number is correct.

Examples from school and society

Transport, energy, environment, health, learning and media use provide useful contexts for asking what data can and cannot show.

Limits and risks

Small samples, measurement errors, missing values, biased selection and unclear definitions reduce the strength of conclusions. These limitations should be named rather than hidden.

Classroom activities

Match data and claims

Decide which data sets could support or challenge a set of claims.

Expected outcome: A claim-evidence map.

Develop possible cause-and-effect explanations

For one relationship, generate several plausible explanations rather than one immediate cause.

Expected outcome: An explanation set.

Distinguish correlation and coincidence

Analyse examples where values move together and decide what can actually be concluded.

Expected outcome: A cautious conclusion.

Make a prediction from data

Use a short time series to predict a near-term value and state assumptions.

Expected outcome: A prediction with conditions.

Support an idea with data

Create one chart that fairly supports a chosen school-related proposal.

Expected outcome: A source-labelled chart.

Present data fairly

Correct a misleading chart by adjusting scale, labels, categories or time range.

Expected outcome: An improved visualisation.

Check gaps and limits

Identify missing variables or weak measurements in a prepared example.

Expected outcome: A limitations list.

Collect and interpret your own data

Gather a small anonymous data set and state what conclusions are justified.

Expected outcome: A mini analysis.

Create a data-supported presentation

Use one table and one visualisation to communicate an idea while acknowledging uncertainty.

Expected outcome: A short presentation.

Reflect on strong claims

Rewrite overconfident causal statements into evidence-based language.

Expected outcome: A set of improved statements.

Self-check

I can choose relevant data for a question.
I distinguish correlation from causation.
I can make cautious cause-and-effect statements.
I can use data for a justified prediction.
I can create a fair visualisation.
I can name important limitations in a data-supported argument.