Representing and processing scientific data — AC9S9I04

• Select a representation that fits the variable types and question.
• Construct tables and graphs with correct labels, units and scales.
• Use suitable descriptive statistics such as mean, median, range or percentage.
• Explain what a representation reveals and what it can hide.

Know independent/dependent variables, basic graph conventions, units and simple calculations of mean and range. Distinguish continuous numerical data from categories.

Representation is a reasoning choice. A line graph is useful for continuous change in an ordered variable such as time; a scatter plot shows association between paired numerical variables; a bar chart compares categories; a table preserves exact values. Statistics compress a dataset but can hide distribution or anomalies.

Axes need variable names and units. Scales should reveal the data honestly. A mathematical relationship or model is useful only when supported by the observed pattern and stated domain; do not force a trend line through data merely because software offers one.

• Any graph is acceptable if the numbers are correct. Graph type must fit variables and question.
• The mean is always the best average. Outliers/skew can make median more informative.
• A graph starting above zero is automatically dishonest. It can be valid if clearly scaled and interpreted, but may exaggerate visual differences.
• Software chooses the science. Tools construct; students must justify the representation.

1. Choose a graph type for time–temperature data.
2. Choose a graph type for three habitat categories and species counts.
3. Add missing units/labels to a supplied graph.
4. Compare mean and median for a dataset containing one extreme value.

1. Create a correctly headed data table.
2. Choose line, scatter or bar representation for three scenarios.
3. Calculate mean, median and range for a dataset.
4. Explain which statistic is most informative and why.
5. Critique a misleading graph scale.
6. Evaluate whether a fitted relationship is justified by the observed data range.

Two students represent the same data: one uses a table and the other a scatter plot with a trend line. Explain which representation is better for checking individual values, which is better for seeing association, and what additional evidence is needed before treating the trend as a causal relationship.

1. How do I choose a graph? Start with variable type and the scientific question.
2. Why include units? A number without its measured quantity/unit can be scientifically ambiguous.
3. Why use statistics? To summarise central tendency or spread, while recognising what summary loses.
4. Does a trend line prove a relationship? It describes a pattern; causation requires stronger evidence.

1. Select and justify the best representation for a supplied investigation.
   Name the variable types and what the graph/table must reveal.
2. Construct a graph from a small dataset and identify one pattern.
   Label axes/units and use a sensible scale before interpreting.
3. Compare two summaries/representations and evaluate which better answers the question.
   Discuss information gained and information hidden.

• I choose representations purposefully.
• I construct correct scientific graphs.
• I select suitable statistics.
• I can critique misleading/weak representations.

Exit ticket: When would a scatter plot tell you more than a table, and what could it hide?

Australian Curriculum v9.0 — AC9S9I04: select/construct tables, graphs, statistics, models and mathematical relationships.

Victoria Levels 9–10 — VC2S10I04: Exact.

NSW Stage 5 — SC5-WS-05: Exact selects and uses tools to process and represent data.

• AC9S9I03 data collection
• AC9S9I05 analysis

• Australian Curriculum v9.0
• Victorian Curriculum F–10 Version 2.0
• NSW Science 7–10 Syllabus (2023)

Curriculum equivalents for Select and construct appropriate representations, including tables, graphs, descriptive statistics...

Mapped skill: select and construct appropriate representations, including tables, graphs, descriptive statistics, models and mathematical relationships, to organise and process data and information

These references identify matching or closely related learning. Curriculum sequence, terminology and depth vary, so teachers should use the mapped skill and lesson difficulty to confirm suitability.

Australian Curriculum v9.0 is the canonical source for this SkillrHub lesson. Victoria and NSW entries name the closest published state codes or outcomes; international entries are planning references rather than claims of identical curricula.