Sampling

Sampling selects a subset of observations to learn about a defined population or process. Valid inference depends on selection, coverage, response, measurement, analysis, and the assumptions connecting the sample with the target question.

Sampling selects observations from a population to learn about a target quantity. The selection process determines which inferences are supported: a large voluntary-response sample can systematically miss people unlike its respondents. Probability sampling provides known selection probabilities, while other designs require different assumptions and may serve exploratory rather than population-estimation purposes.

Sample size affects precision under a design, but cannot automatically repair coverage, nonresponse, or measurement bias. Define the population and question before choosing observations. A sample can represent one quantity reasonably while poorly representing another, and a perfectly recorded answer to the wrong question still fails to inform the intended decision.

When to use it

When interpreting surveys, customer feedback, reviews, or any claim that extends beyond the observations directly collected.

How it can help

Define the population, examine how observations enter the sample, and distinguish sampling uncertainty from systematic coverage or response problems.