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Add English DGB 3 translation

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{{DISPLAYTITLE:DGB 3: Data, Cause and Effect, and Predictions}}
← [[DGB 3. Klasse/en|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 ==

{| class="wikitable"
! 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.

== See also ==

* [[DGB 3: Muster in Datendarstellungen/en|Patterns in Data Visualisations]]
* [[DGB 3: Informations- und Datensuche/en|Independent Information and Data Search]]
* [[DGB 3: Cloudbasierte Systeme/en|Cloud-based Systems]]

[[Kategorie:Digitale Grundbildung]]
[[Kategorie:DGB 3]]

DGB 3: Daten Ursache Wirkung Vorhersagen/en - Versionsgeschichte

Administrator: Add English DGB 3 translation