Year 8 Mathematics · AC9M8ST01

Data Collection Techniques

Choose a data-collection method by matching the question and population, then evaluate practicality, sampling bias, measurement precision, error and ethics

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Key conceptTeach from the board

A population is the whole group of interest. A census attempts to collect data from every member; a sample studies a subset. A census can still suffer coverage, non-response, wording or measurement bias.

Random sampling uses chance to select members. Non-random methods such as convenience sampling can be practical but may systematically over-represent easy-to-reach groups. Sample size does not repair systematic selection bias.

An experiment deliberately imposes a condition or treatment; an observation records what already happens. Observational association does not by itself establish causation.

Digital devices and simulations can improve consistency, but precision is not the same as accuracy. Calibration, resolution, environmental conditions and rounding affect error.

Ethical collection considers consent, privacy, inclusion and respectful use. Sampling decisions also matter in artificial intelligence: biased training data can produce biased models.

Worked examplesWe do

Worked examples

AC9M8ST01 - Data Collection Techniques
Example 1

Classify four scenarios as census, sample, experiment or observation.

Example 2

Identify one likely bias in a shopping-centre survey.

Example 3

Explain one benefit and one limitation of random sampling.

Example 4

Compare precision and accuracy for two measuring devices.

Curriculum examplesCopied content

Australian Curriculum v9.0 — AC9M8ST01: investigate techniques for data collection including census, sampling, experiment and observation, and explain practicalities and implications.

Victorian Curriculum F–10 Version 2.0 — Level 8, VC2M8ST01:Exact.

NSW Mathematics K–10 Syllabus (2022) — Stage 4, MA4-DAT-C-01; MA4-DAT-C-02; MAO-WM-01:Partial. NSW Stage 4 classifies/displays and analyses datasets, but does not reproduce the full national collection-method and sampling-design descriptor as one outcome.

LessonAC v9VictoriaNSW
Data collection techniquesAC9M8ST01VC2M8ST01 — ExactMA4-DAT-C-01; MA4-DAT-C-02; MAO-WM-01 — Partial
Questions and answersWith answers
  1. What is the difference between a census and a sample? A census targets the whole population; a sample studies a subset.
  2. Why can random sampling help? It reduces systematic selection preferences by using a chance mechanism.
  3. What can a well-designed experiment support? Stronger evidence about causal effects than observation alone.
  4. Why can precise data still be wrong? A precise instrument can be miscalibrated or used under biased conditions.
  5. How can sampling affect AI? Unrepresentative training samples can reproduce bias in model behaviour.
Practice and reviewReady for practice
  • A census is automatically unbiased. Coverage, non-response and measurement bias can remain.
  • A huge convenience sample becomes representative. More observations do not remove systematic selection bias.
  • Observation proves cause. Uncontrolled variables may explain an association.
  • More decimal places guarantee accuracy. Precision and accuracy are different.
  • Ethics is separate from statistics. Consent, privacy and inclusion affect responsible, trustworthy data.
Curriculum alignmentStart here
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