Standard error vs standard deviation — the difference.

easy

Answer

$SD: spread of the data (\sqrtvariance) — describes the population.$
$SE: spread of a statistic across hypothetical repeated samples — describes the estimator's precision.$
$\operatorname{SE}(X)\; = \;\mathrm{SD}$
$As n grows, SD stays roughly the same, SE shrinks — that's why bigger samples give tighter estimates without changing what the population looks like.$

Check yourself — multiple choice

Same thing
SD: spread of data; SE: spread of the estimator across samples; $\operatorname{SE}(X)\; = \;\mathrm{SD}$ /√n — bigger n shrinks SE but not SD
SE only for CIs
SD only for chi-square

SD: spread of data; SE: spread of estimator; $\operatorname{SE}(X) = \mathrm{SD}$ /√n.

#estimation#confidence-interval

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