Sampling Survey
Updated 2026-08-15
INTRODUCTION
English translation pending.
CORE DEFINITION
A sample survey draws units from a defined population by a probability design, collects measurements, and estimates population parameters with quantified uncertainty. Its principal designs are simple random, stratified, cluster, and multistage sampling, and its estimates carry sampling errors computed from the design. The core claim is that a well-designed sample of a few thousand can describe a population of millions within a known margin. The qualification is that the claim holds only for the measured variables and only under probability selection; non-probability panels can be large and still unable to support design-based error statements.
SCAFFOLDING EFFECT
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- Choose the design: pick simple, stratified, or cluster sampling based on how the population varies. - Size for precision: derive sample size from the margin of error you can tolerate. - Weight the data: correct for unequal selection and non-response before reporting estimates.
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Random selection makes the sample a scaled-down image of the population in expectation, so sample statistics are unbiased for population parameters. The design determines how variance behaves, which is why stratification reduces error when strata differ and clustering raises it when units within clusters resemble each other. Sample size then sets the width of the interval around the estimate. Weighting restores the intended selection probabilities when response is uneven. Each step converts a physical constraint into a stated uncertainty, which is what distinguishes a survey from an anecdote.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Survey_samplingverified
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