Select and construct appropriate representations to organise and process data and information

Core idea: scientific representations are tools for organising, processing and communicating information. A good representation preserves the meaning of the data and makes relevant patterns or relationships easier to see.

E1 — Spreadsheets

Use spreadsheets to organise datasets, perform calculations such as means or percentage change, filter or sort data and construct appropriate graphs. Spreadsheet output is only as trustworthy as the data, formula and graph choices used.

E2 — Food webs

Food webs model feeding relationships and transfers of matter and energy. Arrows point from food to eater. Producers, consumers and decomposers form interconnected pathways; a food web is more realistic than a single food chain because organisms often have multiple feeding relationships.

E3 — Dichotomous keys

Dichotomous keys classify organisms or objects using a sequence of two contrasting choices. Choices should be based on observable, clearly defined features and should be mutually exclusive. A key can be represented as text, a visual flowchart, an interactive presentation or simple code.

E4 — Mathematical relationships

Analyse primary or secondary data to identify trends, cycles and associations. For example, a line graph of tide height against time can reveal periodic variation. Correlation describes association; it does not by itself prove causation.

E5 — Discrete and continuous data

Discrete data consist of separate counts or categories, such as species count. Continuous data are measured on a scale, such as temperature, time or height. Column graphs suit many categorical/discrete comparisons; line graphs suit continuous change over an ordered variable; scatter plots suit relationships between paired numerical variables; histograms suit distributions of continuous measurements grouped into intervals.

E6 — First Nations astronomical observations

When analysing First Nations Australians’ astronomical observations, acknowledge the specific community, source and cultural context. Long-term observations can reveal recurring seasonal and environmental patterns. Do not treat First Nations knowledges as one uniform system or use contemporary instrument data merely to validate whether those knowledges have value.

• E1: using spreadsheets to aid presentation and analysis of data
• E2: constructing food webs to represent feeding relationships and flows of energy and matter
• E3: constructing visual, interactive or coded dichotomous keys
• E4: analysing primary and secondary data to determine mathematical relationships such as tidal variation
• E5: distinguishing discrete and continuous data and selecting suitable representations
• E6: acknowledging, analysing and interpreting First Nations Australians’ astronomical observations

1. Why does representation choice matter?
   Because the wrong representation can hide patterns, imply relationships that do not exist or mislead the reader.
2. When is a table best?
   When raw values need to be organised precisely before analysis or graphing.
3. When should you use a line graph?
   For continuous change across an ordered variable such as time.
4. When should you use a scatter plot?
   To examine a possible relationship between two paired numerical variables.
5. What do arrows mean in a food web?
   They show energy and matter transfer from the organism eaten to the consumer.
6. What makes a good dichotomous key?
   Two clear, mutually exclusive choices at each step based on observable characteristics.
7. What is secondary data?
   Existing data collected by another source; its context, method and suitability still need evaluation.
8. What is the difference between discrete and continuous data?
   Discrete data are separate counts/categories; continuous data are measured and can take values across a scale.
9. Does correlation prove causation?
   No. Correlation shows association only.
10. How should First Nations astronomical information be used?
    With specific attribution, cultural context, respectful interpretation and attention to the purpose of the knowledge and dataset.

• Joining categories with a line and implying false continuity.
• Reversing food-web arrows.
• Using vague key choices such as “large” or “nice”.
• Assuming an attractive spreadsheet graph is automatically scientifically appropriate.
• Confusing correlation with causation.
• Using misleading graph scales or missing units.
• Generalising one First Nations community’s astronomical knowledge to all First Nations Australians.

Representation quick guide

• Table: organise raw data and exact values.
• Column/bar graph: compare separate categories or discrete groups.
• Line graph: show continuous change over an ordered variable.
• Scatter plot: investigate association between two numerical variables.
• Histogram: display frequency distribution of continuous measurements grouped into intervals.
• Food web: model feeding relationships and energy/matter transfer.
• Dichotomous key: classify using two contrasting choices per step.
• Spreadsheet: organise, calculate, filter and graph data.

Exam checklist

• Identify the data type before choosing a graph.
• Label axes and include units.
• Use a scale that is readable and not misleading.
• Explain why the representation is suitable.
• When interpreting a graph, describe the pattern before explaining it.
• Check secondary-data source, date, location and method.
• For First Nations information, acknowledge the specific source/community and avoid generalisation.

Australian Curriculum: AC9S7I04. Closest broad equivalents include Victorian Curriculum Year 7 Science analysing/representing data and NSW Stage 4 Working Scientifically outcomes. Overseas mappings are approximate because jurisdictions structure scientific inquiry differently.

• AC9S7I03: generate and record data with precision
• AC9S7I05: analyse data and information
• AC9S7U02: food webs and ecosystems