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Data, representation and analysis
Science inquiry — Australian Curriculum v9.0, Year 10 Science.
Achievement focus
Students select equipment and use it efficiently to generate appropriate sample sizes and replicable data with precision. They select and construct effective representations to organise, process and summarise data. They analyse and connect varied data to identify patterns, trends, relationships and anomalies. They evaluate the validity and reproducibility of methods and the validity of conclusions and claims.
AC9S10I03 — Equipment, precision and sample size
Students learn to: select and use equipment to generate and record data with precision to obtain useful sample sizes and replicable data, using digital tools as appropriate.
Learning checkpoints
- Why does “one trial” weakly support a conclusion?
Sample answer: A single run might be a fluke; repeated measurements show consistency and variability.
AC9S10I04 — Representations
Students learn to: select and construct appropriate representations, including tables, graphs, descriptive statistics, models and mathematical relationships, to organise and process data and information.
Learning checkpoints
- When is a line graph usually more appropriate than a bar chart?
Sample answer: When the independent variable is continuous (e.g. time, temperature) and you want to show a trend.
AC9S10I05 — Patterns, trends and anomalies
Students learn to: analyse and connect a variety of data and information to identify and explain patterns, trends, relationships and anomalies.
Learning checkpoints
- What should you do if one point disagrees strongly with the rest?
Sample answer: Check for measurement or recording errors; consider repeating; discuss it honestly rather than hiding it.
AC9S10I06 — Validity of methods and claims
Students learn to: assess the validity and reproducibility of methods and evaluate the validity of conclusions and claims, including by identifying assumptions, conflicting evidence and areas of uncertainty.
Learning checkpoints
- Name one assumption that could invalidate a simple “cause” claim.
Sample answer: Assuming no hidden variable changed (e.g. temperature drift) while only one factor was intentionally varied.