Science inquiry — Advanced

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Level 3: Advanced

Evaluate, justify, synthesise, propose — combine ideas, judge claims and methods (senior secondary style).

1. Evaluate an investigation that uses n = 1 participant and no control to prove a universal health claim.

   A)Acceptable if the effect size is large and the outcome is objectiveB)Very weak — huge variability; need controls, larger samples where ethical, and replicationC)Stronger than a randomised trial when the single participant is carefully chosenD)Free of bias because there is no comparison group to confound the result

2. A company-funded study finds benefits for its product and hides negative trials. This mainly undermines:

   A)Trust and validity of the evidence base (publication bias / selective reporting)B)Only the readability of the discussion sectionC)The statistical power of the positive trials, but not their directional conclusionD)Peer review, which would have caught hidden trials before publication

3. Justify reporting uncertainty (e.g. error bars, range) rather than a single “perfect” number:

   A)Uncertainty discourages replication, so single-point summaries are preferred in scienceB)Repeated measures always remove uncertainty, so reporting a range adds no informationC)Honest reporting reflects measurement limits and helps others judge strength of evidenceD)Error bars imply the true value is unknown, which undermines a clear conclusion

4. Propose one improvement to make a classroom pendulum experiment more reliable (same question investigated).

   A)Time 20 swings but alternate between starting the stopwatch on the first pass and halfway through the cycleB)Use a heavier bob on windy days only, without noting environmental conditionsC)Shorten the string slightly whenever the swing looks wide, to keep amplitude visually similarD)Repeat timing for many swings (or repeat trials), use consistent release, and average to reduce random timing error

5. Synthesise: strong scientific argument in a report needs:

   A)A persuasive tone and confident claims, supported mainly by authorityB)Claims tied to evidence, clear reasoning from data, acknowledgment of limitations, and honest use of sourcesC)Extensive background theory with data mentioned only briefly in an appendixD)Conclusions that match the hypothesis, without discussing mismatches or error

6. Evaluate a conclusion drawn from a convenience sample (e.g. only friends) for a whole year level.

   A)Strong if n > 5B)Valid because friends are honestC)Likely biased — generalising beyond the sampled group is risky without random or structured samplingD)Better than random sampling for fairness

7. Justify plotting error bars (e.g. range or SD) on a mean comparison graph:

   A)Shows spread of data, not just the mean—helps judge overlap and strength of patternB)Replaces raw data tables entirelyC)Proves causationD)Hides outliers always

8. Critique changing the hypothesis after seeing results so it matches the data:

   A)Best practice for high marksB)Risks HARKing (hypothesising after results)—undermines the intended logic of prediction and testC)Required in peer reviewD)Same as preregistration

9. Propose how to improve validity when a measurement tool drifts between sessions:

   A)Ignore drift if trends look smoothB)Always delete half the dataC)Calibrate before sessions, use standards, or correct drift if modelledD)Switch dependent variable mid-trial

10. Synthesise ethics, benefit and risk when using student biometric data in a project:

    A)Collect first, ask laterB)Ethics only apply to animalsC)Minimise sensitive data, secure consent, explain use, store safely, offer opt-out where practicalD)Publish names with heart-rate traces for transparency

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