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.