How do statisticians turn a question into reliable conclusions?
The statistical enquiry cycle, hypotheses, the stages of an investigation, and types of statistical problem.
A focused answer to AQA GCSE Statistics on the statistical enquiry cycle, covering the stages of an investigation, writing a hypothesis, the role of pilot studies, and how the cycle structures a real statistical problem.
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What this dot point is asking
AQA wants you to know the stages of the statistical enquiry cycle, write and refine a clear hypothesis, explain why each stage matters, and recognise that statistical work is a cycle that can be repeated to improve conclusions. This dot point frames the whole "collection of data" module: every other skill here slots into one of the cycle's stages.
The stages of the enquiry cycle
A statistical investigation follows a repeating cycle so that conclusions are based on evidence, not guesswork.
It is drawn as a loop because the evaluation stage often raises new questions, sending you back to plan a better investigation. Each stage maps to skills elsewhere in the course: planning uses hypotheses and sampling design, collection uses questionnaires and sampling methods, processing uses tables and diagrams, interpretation uses averages and comparisons, and evaluation considers bias and reliability.
Writing a hypothesis
A good hypothesis names the variables, names the groups being compared, and is specific enough to test with data. Vague statements such as "gaming is popular" cannot be tested because they do not say what would count as evidence. Writing the hypothesis first also stops you from cherry-picking a conclusion to fit the data afterwards.
Types of statistical problem
Problems usually fall into one of three types: comparing groups (do Year pupils sleep less than Year ?), looking for a relationship between two variables (does more revision lead to higher marks?), or studying change over time (how have temperatures changed across the year?). Identifying the type early helps you choose the right data, the right diagrams, and the right summary statistics: comparisons need averages and spread, relationships need scatter diagrams, and change over time needs a time series.
Pilot studies
Evaluating and refining
The final stage asks whether the hypothesis was supported, whether the methods were sound, and what could be improved. Honest evaluation, including limitations such as a small or biased sample, a low response rate, or unclear questions, is rewarded in the exam, because recognising a method's weaknesses is part of thinking statistically.
Exam-style practice questions
Practice questions written in the style of AQA exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.
AQA 20184 marksA student wants to investigate whether Year pupils sleep less than Year pupils. (a) Write a suitable hypothesis. (b) Describe the stages the student should follow in the statistical enquiry cycle.Show worked answer →
(a) Hypothesis: "Year pupils get fewer hours of sleep on a school night than Year pupils." It names the two groups and the variable, and is testable.
(b) Plan the problem and state the hypothesis; collect data (a stratified sample of both year groups, recording hours slept); process and represent the data (averages, spread, comparative diagrams); interpret and discuss (compare the groups in context); evaluate and refine (assess the method and decide whether to repeat).
Markers reward a specific, testable hypothesis and the five ordered stages described in context.
AQA 20212 marksExplain the purpose of a pilot study in a statistical investigation.Show worked answer →
A pilot study is a small-scale trial of the data collection carried out before the main study.
Its purpose is to check that the questions are clear, the sampling method works, and the data can actually be analysed, so any problems can be fixed cheaply before the full investigation.
Markers reward describing it as a small trial run and giving its purpose (testing and refining the method before the main collection).
Related dot points
- Qualitative and quantitative data, discrete and continuous data, primary and secondary data, and categorical and ranked data.
A focused answer to AQA GCSE Statistics on types of data, covering qualitative and quantitative, discrete and continuous, primary and secondary, categorical and ranked data, and why the type controls which diagrams and calculations you can use.
- Populations, sampling frames, census versus sample, random, systematic, stratified, quota and cluster sampling.
A focused answer to AQA GCSE Statistics on sampling methods, covering populations and sampling frames, census versus sample, and how random, systematic, stratified, quota and cluster sampling work, with the stratified sample calculation.
- Data collection sheets, tally charts, questionnaires, open and closed questions, response boxes and avoiding leading or biased questions.
A focused answer to AQA GCSE Statistics on collecting data, covering data collection sheets and tally charts, open and closed questions, designing non-overlapping response boxes, and how to spot and fix leading or biased questions.
- Explanatory and response variables, controlled and extraneous variables, control groups, and sources of bias in sampling and data collection.
A focused answer to AQA GCSE Statistics on controlling variables and bias, covering explanatory and response variables, controlled and extraneous variables, control groups and matched pairs, and the main sources of bias in sampling and data collection.
Sources & how we know this
- AQA GCSE Statistics (8382) specification — AQA (2017)