Validity, reproducibility and evidence quality — AC9S9I06

• Assess whether a method is valid for its question and reproducible by others.
• Evaluate whether a conclusion is proportional to the data.
• Identify assumptions, uncertainty and plausible alternative explanations.
• Compare conflicting evidence without selecting only the result you prefer.

Recall controls, measurement error, repeats, patterns/anomalies and the difference between association and causation.

Method evaluation asks whether procedures and measurements can answer the question fairly. Conclusion evaluation asks whether the claim follows from the evidence. A precise dataset can still come from an invalid design; a valid design can still have high uncertainty.

Look for sample bias, uncontrolled variables, measurement limitations, selective reporting and assumptions. When studies conflict, compare method quality, samples, contexts and uncertainty. Scientific conclusions should use calibrated language such as “supports”, “is consistent with” or “does not provide sufficient evidence”, rather than automatic “proves”.

• Repeatable means valid. A biased method can repeat consistently.
• A conclusion should restate the hypothesis. It must respond to actual evidence.
• Conflicting evidence means choose the study you trust most. Compare methods and uncertainty systematically.
• Uncertainty makes evidence useless. Uncertainty tells you how strongly to state the conclusion.

1. Identify a validity flaw in a supplied method.
2. Identify what would make the method reproducible.
3. Rewrite “this proves” as a claim proportional to the data.
4. List two reasons two studies might reasonably differ.

1. Distinguish validity from reproducibility.
2. Identify one assumption in a supplied conclusion.
3. Explain the effect of a biased sample.
4. Evaluate whether an anomaly weakens a conclusion.
5. Compare two conflicting evidence summaries.
6. Write a conclusion that explicitly acknowledges uncertainty.

A company claim is supported by its own small experiment, while an independent study reports a smaller benefit and wider uncertainty. Construct an evidence-weighted judgement, specifying which methodological details you would need before accepting or rejecting the claim.

1. Validity or reproducibility? Validity asks whether the method tests the intended question; reproducibility asks whether others can obtain compatible evidence.
2. What is an assumption? Something treated as true for the reasoning to work but not directly established by the data.
3. How should conflicting evidence be handled? Compare quality, context and uncertainty rather than ignoring inconvenient results.
4. Why state uncertainty? It sets an appropriate strength and scope for the conclusion.

1. Evaluate the validity and reproducibility of a supplied method separately.
   Use different evidence for the two judgements.
2. Critique a conclusion that claims causation from observational data.
   Identify the overclaim and at least one alternative explanation.
3. Reconcile two conflicting studies into a cautious overall conclusion.
   Weight method quality, sample/context and uncertainty.

• I assess method validity.
• I assess reproducibility separately.
• I locate assumptions/uncertainty.
• I calibrate conclusions to evidence.

Exit ticket: Give an example of a result that could be reproducible but not valid.

Australian Curriculum v9.0 — AC9S9I06: validity/reproducibility, assumptions, conflicting evidence and uncertainty.

Victoria Levels 9–10 — VC2S10I06: Exact.

NSW Stage 5 — SC5-WS-06 + SC5-DA2-01: Exact/Partial analysis/conclusions and legitimacy of claims together strongly align.

• AC9S9I05 data analysis
• AC9S9I07 arguments

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

Curriculum equivalents for Assess the validity and reproducibility of methods and evaluate the...

Mapped skill: 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

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.