GCSE Statistics₈₃₈₂

Specifications must encourage the application of techniques within the framework of the statistical enquiry cycle using real data taken from authentic contexts.

Specifications must require students to understand the importance of initial planning when designing a line of enquiry or investigation including:

• defining a question or hypothesis (or hypotheses) to investigate
• deciding what data to collect and how to collect and record it giving reasons
• developing a strategy for how to process and represent the data giving reasons.

Specifications must enable students to recognise the constraints involved in sourcing appropriate data including:

• when designing collection methods for primary data
• when researching sources of secondary data, including from reference publications, the internet and the media
• through appreciating the importance of acknowledging sources
• by recognising where issues of sensitivity may influence data availability.

Specifications must require students to understand the ways that data can be processed and presented including:

• organising and processing data, including an understanding of how technology can be used
• generating diagrams and visualisations to represent the data, including an understanding of outputs generated by appropriate technology
• generating statistical measures to compare data, understanding the advantages of using technology to automate processing.

Specifications must require students to understand that results must be interpreted with reference to the context of the problem including:

• analysing/interpreting the diagrams and calculations/measures
• reaching conclusions that relate to the questions and hypotheses addressed
• making inferences and/or predictions
• discussing the reliability of findings.

Specifications must require students to show an understanding of the importance of the clear and concise communication of findings and key ideas, and awareness of target audience.

Specifications must enable students to understand the importance of evaluating statistical work including:

• identifying weaknesses in approach or representation
• suggesting improvements to processes or the presentation
• refining the process to elicit further clarification of the initial hypothesis.