Reading the evidence
Sampling error is the variation between a sample estimate and the population quantity caused by observing only part of the population. It differs from bias and measurement error.
Make it click
Several well-mixed spoonfuls from the same pot contain slightly different proportions by chance. More proper scoops reduce variation without correcting a badly mixed pot.
Now put that into markets
Different comparable market samples can produce different rates even under one stable process. Standard errors quantify this variation under assumptions.
Sampling design, dependence and estimator determine the error. Finite-population and clustered samples require appropriate formulas.
Identify the population, sampling mechanism and effective sample size. Keep non-sampling errors in a separate limitations list.
A small standard error does not address selection bias, data errors or regime change. Precision about a biased target remains misleading.
Takeaway
