M39 · Overview Sampling and interpreting data mind map

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M39 · Overview Sampling and interpreting data mind map

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Data / population

Groups: Population whole; sample part; census all
Kinds: Discrete counted; continuous measured; categorical groups

Collection

Random: Complete frame; random numbers; avoid unwanted duplicates
Systematic: Every kth after random start; watch repeating pattern
Questionnaire: Clear, unbiased wording; complete non-overlapping categories

Evaluate

Bias / size: Large sample alone cannot fix bias; consider non-response
Distribution: Mean not every individual's value; compare centre AND spread

Stratify

Proportions: Higher: Group/total × sample; choose randomly within each group
Allocation: Higher: Rounded allocations total correctly; other biases can remain

Connections

Data / population → Collection: Collection methods determine representation
Data / population → Evaluate: Centre and spread describe a distribution together