HSC Investigating Science Module 5 Scientific Investigations: deep-dive 2026 guide

How Module 5 fits into HSC Investigating Science

Module 5, Scientific Investigations, is the foundation of the whole course. It is where you learn the language and the logic of the scientific method: how to form a hypothesis, design a valid investigation, collect and process data, and judge the quality of evidence. Modules 6, 7 and 8 then apply this machinery to technologies, claims and society, so a marker who sees you misuse "reliable" or "valid" in Module 7 will assume you never mastered Module 5.

NESA examines Module 5 across every section of the paper. The four data-quality terms (reliability, validity, accuracy, precision) appear in multiple choice almost every year, variable identification appears in short answer, and a data-processing question that asks you to find a mean, an uncertainty and an outlier is a recurring 4 to 5 mark item. This guide works through the concepts in the order an investigation actually uses them.

Inquiry questions and hypotheses

A scientific investigation begins with an inquiry question and a hypothesis.

An inquiry question frames what you want to find out, for example "Does caffeine concentration affect the heart rate of Daphnia?"

A hypothesis is a testable, falsifiable prediction of the relationship between the independent and dependent variables. A strong hypothesis is:

• Specific and measurable. "Increasing caffeine concentration increases Daphnia heart rate" beats "caffeine affects animals".
• Falsifiable. There must be a possible result that would prove it wrong.
• Directional where justified. State the expected direction of the effect when prior knowledge supports it.

Variables: the structure of a valid experiment

A controlled experiment changes one thing, measures the result, and holds everything else constant. Three variable types structure this design.

• Independent variable (IV). The one factor the researcher deliberately changes, normally set at several levels (for example 0, 0.1, 0.5 and 1.0 per cent caffeine).
• Dependent variable (DV). The factor measured to see how it responds (Daphnia heart rate in beats per minute).
• Controlled variables. The factors held constant across all groups (water temperature, light, organism age and size, acclimatisation time, the observer counting beats).

A control group is a sample treated identically except that it receives no treatment, or a placebo. The control establishes the baseline against which treated groups are compared. Without it you cannot attribute a result to the IV.

Replication, randomisation and blinding

A single measurement per condition cannot account for variability, so investigations use:

• Replication. Multiple individuals per condition (often 5 to 10) and repeating the whole experiment (often three times) to average out random variation.
• Randomisation. Assigning subjects to groups by chance to reduce selection bias.
• Blinding. Preventing the subject (single-blind) or both the subject and the researcher (double-blind) from knowing the group assignment, which removes the placebo effect and observer expectancy. Double-blind is the gold standard in clinical trials.

The four data-quality terms

This is the most heavily tested vocabulary in the course. The four terms are independent properties: an investigation can be strong on some and weak on others.

• Validity. Whether the investigation tests what it claims to test. Threatened by confounders, sampling bias and instruments that measure the wrong thing. Improved by better design and more controls.
• Reliability. The consistency of repeated measurements. Threatened by random error and inconsistent technique. Improved by more replicates and a standardised procedure.
• Accuracy. How close a measurement is to the true or accepted value. Threatened by systematic error and calibration drift. Improved by calibrating against a reference standard.
• Precision. How close repeated measurements are to each other, regardless of the true value. Threatened by random error and low-resolution instruments. Improved by better technique and finer instruments.

The classic mental model is a dartboard. High accuracy and high precision puts every dart in the bullseye. High precision but low accuracy gives a tight cluster off-centre (a systematic error). High accuracy but low precision scatters the darts around the bullseye so the average is right. Low on both scatters them randomly.

Processing data: error and uncertainty

Once data is collected you must process it. NESA expects you to:

1. Inspect for outliers. A value far from the others (commonly more than 2 to 3 standard deviations) may be a misread or transcription error. Note it, and either re-measure or justify excluding it.
2. Calculate a mean of the valid replicates.
3. State an uncertainty, often half the range of repeated readings or the resolution of the instrument, written as the mean plus or minus the uncertainty.
4. Use sensible significant figures, matching the precision of the instrument.

Two kinds of error must be distinguished:

• Random error. Unpredictable scatter that varies in size and direction between readings (parallax, settling instruments). It degrades precision and is reduced by averaging many repeats.
• Systematic error. A consistent bias in the same direction (an un-zeroed balance, a miscalibrated thermometer). It degrades accuracy, is not reduced by averaging, and is removed only by calibration.

Primary versus secondary data

A complete investigation distinguishes the data you collect yourself from data you draw on.

• Primary data is collected first-hand by the investigator through observation or experiment. It is current and tailored to the inquiry question but limited by your equipment and time.
• Secondary data is sourced from others, such as databases, published studies or Bureau of Meteorology records. It can be large-scale and long-term but you cannot vouch for how it was collected.

A depth study often combines both: primary data from your own measurements, validated against secondary data from a reputable source.

Peer review and reproducibility

The credibility of an investigation does not rest on the author alone. Peer review is the scrutiny of a method and findings by independent experts before publication, which filters out weak design and overstated claims. 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 regardless of how striking it first appeared. Together they are the self-correcting machinery that distinguishes science from a single unverified report.

Risk assessment and ethics

Before any first-hand investigation you must complete a risk assessment: identify hazards (chemicals, heat, biological material, electrical equipment), assess the likelihood and severity, and state control measures (personal protective equipment, ventilation, safe disposal). Investigations involving animals or humans require ethical approval, informed consent, and consideration of animal welfare under the relevant codes. Markers reward students who name a specific hazard and its specific control rather than writing "be careful".

Designing and evaluating a depth study

The Module 5 skills come together in the depth study, an extended student investigation. A strong depth study walkthrough has a predictable shape:

1. Inquiry question and falsifiable hypothesis.
2. Variable table. IV with its levels, DV with its units and instrument, and a thorough list of controlled variables.
3. Method with enough detail and replication for reproducibility, plus a control group.
4. Risk assessment naming specific hazards and controls.
5. Data processing with means, uncertainties and outlier handling.
6. Evaluation that explicitly judges the investigation's validity, reliability, accuracy and precision, and proposes improvements.

The evaluation is where marks are won. Do not just assert "my experiment was reliable". State why: "Three replicates per concentration produced readings within 2 beats per minute of each other, indicating high reliability; however validity was limited because room temperature was not actively controlled, a confounder that future work should hold constant with a water bath."

Common HSC Module 5 examiner traps

• Using "reliable" when you mean "valid", or "accurate" when you mean "precise". Markers penalise loose vocabulary.
• Claiming more replicates improve validity. Replication improves reliability and precision; validity is about design.
• Confusing controlled variables with the control group.
• Forgetting to state an uncertainty or to handle an outlier in a data-processing question.
• Writing "wear safety goggles, be careful" instead of naming a specific hazard and a specific control.

Check your knowledge

A mix of definitional, design and data-processing questions covering this topic. Answer all under exam conditions, then check against the solutions block.

1. Define reliability and validity, and explain the relationship between them with an example of an investigation that is reliable but not valid. (4 marks)
2. A researcher investigates whether caffeine concentration affects the heart rate of Daphnia. Identify the independent, dependent and at least three controlled variables, and state the role of the control group. (5 marks)
3. Distinguish between accuracy and precision. Using the data sets below, classify each as accurate, precise, both or neither, given a true value of 1.00 g/mL. (a) 1.00, 1.00, 1.01, 1.00. (b) 0.85, 0.86, 0.85, 0.86. (c) 0.90, 1.05, 0.95, 1.10. (5 marks)
4. A student measures a reaction time five times: 0.42, 0.41, 0.43, 0.58, 0.42 s. (a) Identify and justify any outlier. (b) Calculate the mean of the valid values. (c) State the uncertainty and report the value correctly. (d) Identify one random and one systematic source of error. (6 marks)
5. Explain the difference between primary and secondary data, giving one advantage and one limitation of each. (4 marks)
6. A class investigates whether a new fertiliser increases tomato yield but omits a control group. Explain why a control group is essential and what the omission prevents the class from concluding. (4 marks)
7. Distinguish between random and systematic error, state which data-quality property each affects, and describe how each is reduced. (4 marks)
8. A depth study tests whether light intensity affects the rate of photosynthesis in pondweed, measured by bubbles of oxygen per minute. The student takes one reading at each of three light intensities and concludes that higher light always increases photosynthesis. Evaluate the validity and reliability of this design and propose two improvements. (6 marks)