Stichprobenverteilungen

Stichprobenverfahren (sampling methods) are systematic procedures for selecting a subset of individuals or items from a larger population in order to estimate characteristics of the whole. The aim is to obtain accurate, efficient information while limiting cost and effort.

They are broadly categorized into probability (random) sampling and non-probability sampling. Probability sampling assigns a known, non-zero chance to every member of the population, enabling formal inference. Non-probability sampling relies on subjective judgment or practical considerations, and inferences about the population are typically less generalizable.

Probability sampling includes simple random sampling, where every member has an equal chance of selection; systematic sampling, which selects units at regular intervals; stratified sampling, which divides the population into homogeneous subgroups and samples within each subgroup; and cluster sampling, which groups units into clusters and samples whole clusters or within-cluster units. These methods are valued for their potential to produce representative samples and allow estimation of sampling error.

Non-probability sampling includes convenience sampling (easy access), judgmental or expert sampling (based on expertise), quota sampling (ensuring certain characteristics are represented), and snowball sampling (participants recruit others). Such methods can be faster or cheaper but typically provide weaker generalizability.

Key design considerations include the sampling frame, sample size, and strategies to minimize bias and nonresponse. Properly designed Stichprobenverfahren support reliable estimation of population parameters, with calculable uncertainty through standard errors and confidence intervals. Limitations often arise from nonresponse, coverage gaps, or cost constraints, which can affect representativeness and validity of conclusions. Applications span public opinion polling, market research, epidemiology, and quality control.