Analyse methods, conclusions and claims for assumptions, possible sources of error, conflicting evidence and unanswered questions

1. Data quality and unanswered questions (E1)

Useful indicators include consistency across repeated trials, spread, outliers, relevance to the aim, sufficient repeats and whether measurements directly answer the investigation question. A gap in these indicators often reveals an unanswered question.

2. Evaluating methods and assumptions (E2)

An assumption is something accepted as true without being directly tested—for example, that temperature stayed constant or a balance remained calibrated. Identify variables that should have been controlled, then explain how the method could be improved and why.

3. Conclusions, claims and premises (E3)

A conclusion should be supported by evidence, answer the aim and stay within the scope of the sample and method. Watch for premises taken for granted, such as assuming correlation proves causation or assuming all trials were fair without evidence.

4. Spread of repeated measurements (E4)

Spread describes how far repeated measurements are separated. A simple measure is range = maximum − minimum. Small spread supports high precision/consistency; it does not automatically prove accuracy.

5. Sources of error and improvements (E5)

Random variation changes unpredictably between trials, such as slight reaction-time differences. Repeats can reduce its influence. Systematic error shifts results consistently, such as a miscalibrated instrument; it must be corrected at the source.

6. Conflicting evidence (E6)

When evidence disagrees, compare sample size, controls, spread, relevance, method quality, possible bias and whether the studies tested the same conditions. Conflicting evidence should be evaluated, not ignored.

• E1: indicators of data quality and unanswered questions
• E2: evaluate methods, assumptions and justified improvements
• E3: analyse conclusions, claims and premises
• E4: consider spread of repeated measurements
• E5: identify sources of error and improve methods
• E6: identify supporting evidence and evaluate conflicting evidence

1. What makes data high quality? Consistent, relevant repeated measurements with manageable spread, investigated outliers and a method that directly addresses the aim.
2. Does small spread prove accuracy? No. It supports precision; measurements can be tightly clustered yet systematically wrong.
3. What is an assumption? A condition treated as true without direct evidence or testing.
4. Why do uncontrolled variables matter? They can provide another explanation for changes in the dependent variable, weakening validity.
5. How do repeats help? They reveal variation and reduce the influence of random error when results are summarised appropriately.
6. Can repeats remove systematic error? No. Calibration, replacement or method correction is needed.
7. What makes a conclusion strong? It is supported by the data, matches the aim, stays within the evidence and acknowledges limitations.
8. What should you do with an outlier? Investigate why it occurred and, where possible, repeat the measurement before deciding how to treat it.
9. How should conflicting evidence be handled? Compare evidence quality and context rather than choosing the result you prefer.
10. What is a good unanswered question? A follow-up question that targets a genuine gap in what the original investigation tested.

• Saying “the data are accurate” only because repeated values are close together.
• Listing a limitation without explaining its effect on the conclusion.
• Saying “more trials” fixes every problem, including systematic bias.
• Deleting outliers automatically.
• Accepting a conclusion simply because it matches the hypothesis.
• Ignoring conflicting evidence.

Data Quality

• Check repeated-trial consistency.
• Calculate or compare spread.
• Identify and investigate outliers.
• Ask whether the data actually answer the aim.

Method Quality

• Identify the independent, dependent and controlled variables.
• Find assumptions and uncontrolled variables.
• Check whether the steps are reproducible and measurements appropriate.
• Suggest specific improvements with reasons.

Error

• Random variation: unpredictable trial-to-trial differences; use repeats and standardised technique.
• Systematic error: consistent bias; calibrate, replace or redesign.

Claims

• Trace each claim to supporting evidence.
• Check whether premises are supported and relevant.
• Do not treat correlation as causation without an appropriate design.
• Match the scope of the conclusion to the sample and conditions.

Conflicting Evidence

• Compare sample size, controls, spread, bias, methods and relevance.
• Acknowledge disagreement and explain why one evidence set may be stronger.

• AC9S7I05 — Analyse data and information
• AC9S7I07 — Construct evidence-based arguments