QCAA Agricultural Science EA study plan

Map the Unit 3 syllabus into a one-page checklist of every data-test skill (graph interpretation, calculation, error analysis, validity and reliability judgement).

Define and write out in your own words the core experimental-design terms: independent, dependent and controlled variables, control, replication, randomisation, accuracy, precision, validity, reliability.

Practise 10 unit-conversion and significant-figure drills drawn from agricultural contexts (yield per hectare, stocking rate, fertiliser rate, moisture content).

Build a formula sheet for common agricultural calculations (yield, gross margin components, feed conversion, stocking rate, percentage change) with units stated for each.

Interpret at least 12 tables and graphs (line, bar, scatter, box plot) from agricultural trial scenarios and write a one-sentence trend statement for each.

Calculate and interpret measures of central tendency and spread (mean, median, range, standard deviation) from a raw dataset you construct yourself.

Identify sources of error and suggest one improvement for each of six short experimental vignettes you write.

Explain how randomisation, replication and blocking reduce bias, using a field-trial example you design.

Draft a full IA1-style data test under timed conditions (60 minutes) using only stimulus you author yourself.

Mark your own draft against a criteria-style rubric you write, listing specific evidence for each criterion.

Rewrite your three weakest responses to show explicit reasoning chains (claim, evidence from data, justification).

Practise describing uncertainty: distinguish random from systematic error and state the effect of each on results.

Write a complete IA2-style research question and hypothesis linking an agricultural variable to a measurable outcome.

Produce a risk assessment and a justified methodology (apparatus, sampling, replication, controls, measurement precision) for your chosen experiment.

Design a raw data table with correct headings, units and appropriate decimal places.

Explain how you would evaluate validity and reliability of your own design in 150 words.

Generate a realistic dataset for your proposed experiment and process it (means, spread, graph, anomaly identification).

Write a results section that describes patterns without interpreting them, then a separate discussion that interprets them.

Evaluate your design against validity and reliability criteria and propose two specific improvements with justification.

Complete one timed 45-minute data-analysis set to maintain IA1 fluency.

Select a Unit 4 research investigation topic and write a focused research question that is answerable from credible secondary sources.

Locate and screen at least eight credible sources (peer-reviewed, government, industry) and record author, year, purpose and reliability for each.

Synthesise findings into a claim-evidence-reasoning structure, noting where sources agree and conflict.

Draft a reference list in a consistent citation style and check every in-text citation matches.

Write the full IA3 research investigation draft (introduction, analysis, evaluation, conclusion) within the word limit.

Evaluate the strength of your evidence base, explicitly identifying limitations, bias and gaps in the sources.

Justify your conclusion by linking each point back to a specific cited source.

Peer-review or self-review the draft against a criteria-style rubric and log every required change.

Revise the IA3 draft to address every logged improvement action and verify citation accuracy.

Re-sit one timed IA1 data test and one condensed IA2 methodology task to confirm retained skills.

Consolidate a one-page summary of experimental-design principles and evaluation language for final recall.

Complete a final self-audit against both domain checklists and list any remaining weak points with a revision action.