Unit 3 IA2: Student Experiment - Design, Analysis and Evaluation
What the student experiment demands
The IA2 is your own investigation: a research question, a modified or extended experiment, raw data, processing, analysis, evaluation and a suggested improvement. Concepts you must control: formulating a testable hypothesis with a predicted direction; identifying variables and operationalising them (how exactly will you measure leaf area or soil moisture?); replication and randomisation to reduce bias; calibration and consistent technique; appropriate processing (means, standard deviation, percentage change, graphing with uncertainty); and a reasoned evaluation of reliability and validity.
Traps to watch
- Unmeasurable hypothesis: "Fertiliser helps plants" is not testable. Specify rate, species, response and direction.
- Confusing reliability with validity: reliability is repeatability and consistency; validity is whether you measured what you intended. Replication improves reliability; a flawed measurement method undermines validity.
- No raw data: markers need the original observations, not just processed means.
- Unjustified improvement: "use more replicates" is weak. Say how many, why, and what error it reduces.
- Ignoring anomalies: outliers must be identified and either justified or excluded with reasoning, not silently deleted.
- Missing safety or ethical considerations: chemical handling, animal welfare, biosecurity.
Worked rule of thumb
Use the question–method–evidence–judgement chain. Example: question - does mulch depth affect soil moisture retention? Method - five pots per depth (0, 2, 4 cm), randomised positions, same watering volume, moisture measured by oven-dry subsample. Evidence - means plus standard deviation, graphed with error bars. Judgement - state whether the hypothesis is supported, cite the difference, then name one limitation (small pot scale) and one improvement (field trial with more replicates). One limitation must be explicitly linked to validity and one to reliability.
Preparing
Draft your method so another student could repeat it exactly. Pre-plan processing before collecting data. Write evaluation sentences that name the flaw, its effect on results, and the fix.
Unit 4 IA3: Research Investigation - Evidence, Claims and Agricultural Context
What the research investigation requires
The IA3 is a research investigation into an agricultural question, presented as a claim, evidence and reasoning argument. Concepts you must understand: framing a focused research question; locating and evaluating credible sources (peer-reviewed journals, government agricultural agencies, industry reports) over unreliable ones; distinguishing primary from secondary data; synthesising multiple sources rather than listing them; and evaluating the strength, currency and relevance of evidence. Contexts may include soil health and carbon, water-use efficiency, integrated pest management, animal welfare, genetic technologies, or sustainable intensification.
Traps to watch
- Topic instead of question: "Soil carbon" is a topic. "How does stubble retention affect soil organic carbon in grain cropping?" is researchable.
- Source dumping: paragraphs that summarise one source after another without comparison earn few marks. Synthesise: where do sources agree, conflict, and why?
- Ignoring bias and context: a fertiliser company report and a peer-reviewed trial carry different weight. Note funding, sample size and region.
- Confusing claim with evidence: a claim is the assertion; evidence is the data or findings supporting it. Keep them distinct in your argument.
- Overgeneralising: results from one soil type or climate may not transfer. State the boundary conditions.
- Uncritical acceptance: evaluate limitations of your sources, not just their conclusions.
Worked rule of thumb
Use the CER + evaluation structure: Claim (one sentence answering the question), Evidence (two or three specific findings with source type and year), Reasoning (the agricultural mechanism linking evidence to claim), Evaluation (strength of evidence, conflicting findings, limitations, and confidence level). For example, claim that controlled-traffic farming reduces soil compaction; evidence from replicated trials showing lower penetration resistance; reasoning about axle load and pore space; evaluation noting soil-type dependence. Each paragraph should advance the argument, not merely report.
Preparing
Build a source table (author, year, type, finding, limitation) before writing. Ensure every claim is matched to evidence and every source is evaluated, not just cited.