How should I use these HSC Investigating Science practice questions?

Work in module blocks under timed conditions, allowing roughly 1.5 to 2 minutes per mark. Write full responses, then mark against the solutions block. Investigating Science rewards explicit use of the method vocabulary (independent variable, controlled variable, reliability, validity, falsifiability) and named Australian case studies, so practise weaving those in rather than writing generic answers.

What does NESA reward in Investigating Science extended responses?

Markers reward criteria applied to specific named cases, correct use of the evaluation verbs, and explicit reference to the scientific method and evidence quality. 'Evaluate' needs a judgement, 'assess' needs strengths and limitations weighed, and 'justify' needs evidence-backed reasoning. Naming a case study such as CSIRO Wi-Fi, the cochlear implant or Wakefield's retracted paper beats a generic answer.

How is the Investigating Science exam structured?

The HSC Investigating Science paper is 2 hours plus 5 minutes reading time for 100 marks. Section I is 20 multiple choice for 20 marks, Section II is short and extended responses for 55 marks across the four modules, and Section III is a single 25-mark extended response, usually a case-study evaluation drawing on the whole course.

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HSC Investigating Science practice questions for 2026 (Modules 5-8)

Practice questions for HSC Investigating Science, grouped by module (Scientific Investigations, Technologies, Fact or Fallacy, Science and Society). Modelled on NESA exam patterns, with worked solutions, to drill the method vocabulary, evidence evaluation and Australian case studies markers reward.

Generated by Claude Opus 4.714 min readNESA-INS-12Updated 2026-05-24

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How to use this question bank
Module 5: Scientific Investigations (1-7)
Module 6: Technologies (8-13)
Module 7: Fact or Fallacy (14-20)
Module 8: Science and Society (21-26)
Worked solutions

How to use this question bank

HSC Investigating Science is a 2-hour paper that tests the scientific method through case studies rather than a single discipline. These practice questions span the four Year 12 modules and are modelled on NESA paper patterns.

Three rules for practising:

Use the precise vocabulary. Markers test the difference between reliability and validity, and accuracy and precision, every year. Loose use of these terms loses marks.
Name your case studies. Generic answers are penalised. Cite specific Australian examples (CSIRO Wi-Fi, the cochlear implant, the HPV vaccine, Wakefield's retracted paper, the IPCC, NHMRC) wherever relevant.
Answer the verb. "Evaluate" requires a judgement, "assess" requires strengths and limitations weighed, "justify" requires evidence-backed reasoning, "distinguish" requires the points of difference.

Module 5: Scientific Investigations (1-7)

Distinguish between reliability and validity in a scientific investigation, using an example to illustrate each. (4 marks)
A researcher investigates whether water temperature affects the rate of an enzyme reaction. Identify the independent, dependent and three controlled variables, and state the role of the control group. (5 marks)
Explain the difference between accuracy and precision, with a target-style example showing that they are independent. (3 marks)
A student measures the mass of a sample five times: 12.4, 12.5, 12.4, 13.1, 12.5 g. (a) Identify and justify any outlier. (b) Calculate the mean of the valid values. (c) State the uncertainty and report the result. (4 marks)
Explain the difference between random and systematic error, stating which data-quality property each affects and how each is reduced. (4 marks)
Distinguish between primary and secondary data, giving one advantage and one limitation of each. (4 marks)
Explain the roles of peer review and reproducibility in establishing the credibility of a scientific finding. (4 marks)

Module 6: Technologies (8-13)

Using a named Australian example, explain how scientific knowledge enabled the development of a technology. (5 marks)
The CSIRO Wi-Fi patent is often cited in Investigating Science. Outline the relationship between the underlying science and the resulting technology, and one social or economic impact. (4 marks)
Explain how the multi-channel cochlear implant illustrates the interaction between scientific research and technological development. (4 marks)
The HPV vaccine was developed from Australian research. Describe how this case shows science and technology informing public health policy. (4 marks)
All technologies have limitations. Using one named example, assess one limitation of a scientific technology. (4 marks)
Explain why the development of a technology often depends on advances in more than one field of science. (3 marks)

Module 7: Fact or Fallacy (14-20)

Explain the concept of falsifiability and why it is important for distinguishing science from pseudoscience. (4 marks)
Using one named scientific and one named pseudoscientific claim, distinguish between scientific and pseudoscientific claims. (6 marks)
Distinguish correlation from causation, with one example of a correlation that is not causal. (3 marks)
State four Bradford Hill criteria and explain how they supported the conclusion that smoking causes lung cancer. (5 marks)
Rank these designs from strongest to weakest evidence and justify: case report, cohort study, randomised controlled trial, systematic review. (4 marks)
A claim states that children diagnosed with autism shortly after an MMR vaccine prove the vaccine causes autism. (a) Name the logical fallacy. (b) Give the correct interpretation. (3 marks)
A media article claims "drinking coffee reduces heart-disease risk" based on a single observational study. Evaluate this claim. (5 marks)

Module 8: Science and Society (21-26)

Explain the role of research ethics in human and animal investigations, with reference to informed consent and one Australian framework. (4 marks)
Explain what a conflict of interest is in scientific research and how it can be managed. (4 marks)
Using a named example, explain how scientific evidence informs evidence-based policy in Australia. (5 marks)
The IPCC synthesises global climate science. Explain how scientific consensus is established and why it is more reliable than a single study. (5 marks)
Explain one benefit and one challenge of integrating Indigenous knowledge with Western science. (4 marks)
Assess the role of clear science communication in shaping public understanding and behaviour, using one example. (5 marks)

Worked solutions

Solutions

Q1: Reliability is the consistency of repeated measurements; a reliable investigation gives similar results when repeated under the same conditions. Validity is whether the investigation tests what it claims to test, isolating one independent variable with controlled variables held constant. Example of reliability: measuring the boiling point of water five times and getting 100.1, 100.0, 100.2, 100.1, 100.0 degrees Celsius, a tight cluster. Example of validity: testing whether sunlight affects growth using the same plant species, soil and watering so the effect is attributable to light rather than to a confounder. An investigation can be reliable without being valid but cannot be valid without being reliable.
Q2: Independent variable: water temperature, set at several levels (for example 10, 20, 30, 40 degrees Celsius). Dependent variable: rate of reaction, measured as volume of product per minute. Controlled variables (three): enzyme concentration, substrate concentration, pH, and reaction time (any three held constant). Control group: a reaction held at a single reference temperature, treated identically, establishing a baseline rate for comparison. This isolates the effect of temperature on reaction rate.
Q3: Accuracy is how close a measurement is to the true or accepted value; precision is how close repeated measurements are to each other regardless of the true value. Target example: darts tightly clustered in one corner are precise but not accurate (a systematic offset); darts scattered evenly around the bullseye are accurate on average but not precise (random scatter). The example shows the two are independent properties of a measurement set.
Q4: (a) The value 13.1 g sits well above the cluster near 12.45 g and is a probable outlier, likely a misread; flag and exclude. (b) Mean of the valid four: $(12.4 + 12.5 + 12.4 + 12.5)/4 = 12.45$ g. (c) Range of valid values is $12.5 - 12.4 = 0.1$ g, so uncertainty is about $\pm 0.05$ g; report as $12.45 \pm 0.05$ g.
Q5: Random error is unpredictable scatter that varies in size and direction between readings (parallax, settling instruments); it degrades precision and is reduced by averaging repeated measurements. Systematic error is a consistent bias in the same direction (an un-zeroed balance, a miscalibrated meter); it degrades accuracy, is not reduced by averaging, and is removed by calibration against a reference standard.
Q6: Primary data is collected first-hand by the investigator through observation or experiment; secondary data is sourced from others such as databases or published studies. Primary data advantage: tailored to the inquiry question and current; limitation: limited by your equipment and sample size. Secondary data advantage: can be large-scale and long-term, such as decades of Bureau of Meteorology records; limitation: you cannot verify how it was collected or control its quality.
Q7: Peer review is the scrutiny of a method and findings by independent experts before publication; it filters out weak design and overstated claims and is a precondition for a finding entering the scientific record. Reproducibility is the ability of independent researchers to repeat the methodology and obtain consistent results; a finding that cannot be reproduced is treated with caution. Together they are the self-correcting machinery that distinguishes a credible scientific finding from a single unverified report.
Q8: Award for any well-supported named example. Model answer (cochlear implant): research into the physiology of the cochlea and how the auditory nerve encodes sound as patterns of electrical impulses provided the scientific knowledge. Graeme Clark's team at the University of Melbourne applied this to design a multi-channel implant that stimulates different regions of the cochlea at different frequencies, translating sound into nerve signals. The science of neural encoding directly enabled the technology, which restored hearing to people with profound deafness.
Q9: The underlying science was signal processing, specifically the use of fast Fourier transform mathematics to handle the problem of radio signals bouncing off indoor surfaces and arriving as overlapping echoes (multipath). CSIRO researchers applied this to allow fast, reliable wireless transmission indoors, which became foundational to wireless networking. Social and economic impact: the patent was enforced internationally and returned a large sum to Australian publicly funded research, and the technology underpins everyday wireless connectivity.
Q10: The cochlear implant shows a two-way interaction. Scientific research into how the cochlea and auditory nerve convert sound into electrical signals provided the knowledge base. Translating that into a working multi-channel device required engineering advances in miniaturised electronics, biocompatible materials and surgical technique. In turn, data from implant recipients fed back into further research on neural encoding. The case shows technology and science co-developing rather than one simply applying the other.
Q11: Research by Ian Frazer and Jian Zhou at the University of Queensland produced virus-like particles that became the basis of the HPV vaccine. Clinical trials provided the quantitative evidence of efficacy in preventing infection with cancer-causing HPV types. This evidence informed public health policy: the vaccine was incorporated into Australia's National Immunisation Program. The case shows scientific research generating a technology whose evidence base then drove a population-level policy decision.
Q12: Award for any named example with a genuine limitation. Model answer (a research reactor such as OPAL): a major benefit is the production of medical radioisotopes for diagnosis and treatment, but a limitation is that the technology produces radioactive waste requiring long-term safe storage, and it depends on a secure supply of fuel and strict regulatory oversight. Assessing the limitation means weighing the substantial medical benefit against the waste-management and safety obligations, which are manageable but real and ongoing.
Q13: Technologies usually integrate knowledge from several sciences because a working device must satisfy constraints that no single field covers. For example, a medical implant draws on biology (how the body responds), chemistry and materials science (biocompatible materials), and physics and engineering (electronics and power). Progress in any one field can be the bottleneck, so a technology often advances only when several fields are sufficiently developed.
Q14: Falsifiability, argued by Karl Popper in 1934, is the principle that a claim is scientific only if some possible observation or experiment could prove it wrong. It is important because science advances by attempting to disprove hypotheses; a claim that survives serious attempts at disproof gains credibility, while a claim no result could disprove cannot be tested and is empirically empty. Pseudoscience often uses scientific-sounding language for claims that are unfalsifiable, which is why falsifiability is the most cited single test for distinguishing the two.
Q15: Scientific claim, vaccination: falsifiable (an outbreak in a vaccinated population would disprove efficacy), peer reviewed (thousands of studies), replicable, quantitative (efficacy with confidence intervals), mechanistic (triggers immune memory) and provisional. Pseudoscientific claim, homeopathy: practically unfalsifiable (failures blamed on non-individualised remedies), no plausible mechanism (extreme dilutions leave no molecules of the original substance), no consistent peer-reviewed positive effect in meta-analyses, and no genuine updating of theory in response to evidence. Comparison: vaccination satisfies every criterion of science, homeopathy fails each one, which is what distinguishes a scientific from a pseudoscientific claim.
Q16: A correlation is a statistical association between two variables; causation is a directed relationship where changing one produces a change in the other. All causation produces correlation, but not all correlation indicates causation. Example: ice cream sales correlate with drowning deaths because both rise in hot weather, the confounder; eating ice cream does not cause drowning.
Q17: Any four. Strength: smokers had over ten times the lung cancer risk. Consistency: replicated in dozens of studies across many countries. Temporal sequence: smoking preceded cancer onset by decades. Dose-response: more pack-years gave higher risk. Plausibility: tobacco smoke contains over 60 carcinogens that damage DNA. Together the criteria established causation from observational data, since deliberately making people smoke in a randomised trial would be unethical.
Q18: From strongest to weakest: systematic review, randomised controlled trial, cohort study, case report. The systematic review combines and quality-weights all relevant studies, best controlling confounders, reverse causation and chance. The RCT randomises participants, balancing confounders so outcome differences are attributable to the treatment. The cohort study follows groups over time and can show association but cannot fully control confounders. The case report describes a single patient and is hypothesis-generating only.
Q19: (a) Post hoc ergo propter hoc, treating a temporal sequence as proof of causation. (b) Autism is typically diagnosed at around the same age that MMR is routinely given, so the two events coincide in time without one causing the other. Large studies of hundreds of thousands of children show no causal link.
Q20: A single observational study sits low on the hierarchy of evidence and can show correlation but not causation. Coffee drinkers differ from non-drinkers in age, exercise and diet, so without statistical adjustment the association may reflect lifestyle confounders rather than coffee. Reverse causation is possible: people with heart problems may have cut down on coffee, creating an apparent protective effect. Publication bias may also mean one positive study was selected from many null ones. Judgement: the headline is not warranted; a fair framing is that coffee consumption is associated with lower risk in this cohort, but causal inference requires randomised trials or consistent observational evidence with a mechanism, supported by a meta-analysis.
Q21: Research ethics ensures investigations protect participants and animals. For human research, informed consent means participants are told the purpose, risks and their right to withdraw, and agree voluntarily. For animal research, the principles of replacement, reduction and refinement minimise harm. An Australian framework is the NHMRC National Statement on Ethical Conduct in Human Research, which requires ethics-committee approval before a study begins. Ethics protects participants and also maintains public trust in science.
Q22: A conflict of interest exists when a researcher has a financial, professional or personal interest that could bias their work, for example funding from a company whose product is being tested. It can distort study design, analysis or the reporting of results. Management strategies include mandatory disclosure of funding and interests, independent peer review, pre-registration of study protocols so outcomes cannot be selectively reported, and independent replication. Disclosure does not remove the conflict but allows readers to weigh the evidence accordingly.
Q23: Award for any well-supported example. Model answer (tobacco plain packaging): decades of consistent evidence that smoking causes disease, established through the Bradford Hill criteria, supported Australia's National Tobacco Strategy and the world-first plain-packaging legislation. The policy was based on a strong evidence base rather than opinion, and its effect on smoking rates was then monitored. The case shows scientific evidence translated into regulation, with ongoing data collection to evaluate the policy.
Q24: The IPCC synthesises the work of thousands of scientists by reviewing and assessing the published peer-reviewed literature, with multiple rounds of expert and government review. Scientific consensus is established when many independent lines of evidence (temperature records, ice cores, ocean data, models) converge on the same conclusion and survive repeated scrutiny. It is more reliable than a single study because consensus integrates many studies, averages out the errors and biases of any one, and reflects evidence that has been replicated and reviewed, rather than a single result that may not hold up.
Q25: Benefit: Indigenous knowledge accumulated over tens of thousands of years offers detailed, place-based understanding of ecosystems, fire management and species behaviour that can complement and extend Western scientific data, for example cultural burning practices now used in land management. Challenge: the two knowledge systems differ in how they are recorded, validated and owned, so integration requires genuine partnership, respect for cultural protocols and intellectual property, and care not to extract Indigenous knowledge without consent or benefit-sharing.
Q26: Clear science communication shapes whether the public understands and acts on evidence. Example: during the COVID-19 pandemic, communication that explained uncertainty honestly, gave specific guidance and used trusted messengers influenced behaviours such as vaccination and distancing, whereas mixed or jargon-heavy messaging fed confusion and mistrust. Assessment: good communication is essential because even strong evidence has no effect if the public cannot understand or trust it, but it must balance simplicity with honesty about uncertainty; overstating certainty can backfire when advice later changes. On balance, clear, honest communication is a decisive factor in translating science into public benefit.