Data Types, Collection and Presentation
Distinguish qualitative and quantitative data, select suitable collection methods and interpret tables and graphs.
How to prepare for the written examination
Move from accurate theory to a scenario-specific mechanism, consequence, evidence and judgement. Complete all practical performance and NEA work only through authorised centre arrangements.
Core concepts
Concept 1
Quantitative data are numerical; qualitative data describe qualities, experiences or observations.
Exam cue: Identify the variables, units, source and collection method before interpreting.
Concept 2
Collection method, sample, protocol and measurement precision affect what data can support.
Exam cue: Choose a presentation that suits categories, continuous values or change over time.
Concept 3
A suitable table or graph should match the variable type and make comparison clear.
Exam cue: State a precise pattern using figures and units from the supplied data.
Risk pitfalls and guardrails
Calling all subjective evidence invalid.
Guardrail: Do not replace explanation with a named sport, an undated current example or practical work that belongs to NEA.
Describing a graph without using its scale or values.
Guardrail: Do not replace explanation with a named sport, an undated current example or practical work that belongs to NEA.
Inferring causation from a simple association.
Guardrail: Do not replace explanation with a named sport, an undated current example or practical work that belongs to NEA.
Memory anchors
Quantitative PE data
Numerical measurements or counts used in physical-activity analysis.
Qualitative PE data
Descriptive observations, views or categories used in analysis.
Data variable
A characteristic measured, controlled or compared.
Data presentation choice
Select a table or graph suited to the variables and comparison.
Data-pattern statement
Describe direction, size and relevant comparison with values and units.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
Which is an example of quantitative data in PE?
Which is an example of qualitative data?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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