statistikkrammer

Statistikkrammer is a term used to describe the set of methodological frames or scaffolds that organize statistical work. The concept refers to the structured context in which data are collected, analyzed, and interpreted, and it encompasses choices about design, measurement, modeling, and reporting. A statistikkrammer typically includes several interrelated components that guide decisions throughout a project.

Key components include the research design frame (whether the study is experimental or observational, how causal questions are approached, and how confounding is managed); the sampling frame (defining the population, selecting sampling methods, and establishing inclusion criteria); and the measurement frame (defining variables, measurement scales, data quality standards, and handling of missing data). The analytical frame covers modeling choices and underlying assumptions, while the inference frame addresses uncertainty quantification, such as confidence or posterior intervals and criteria for decision making. A validation and reproducibility frame emphasizes cross-validation, replication, and thorough documentation. The reporting frame focuses on transparent communication, data and code availability, and clarity about limitations.

Common examples of statistical frameworks include classical frequentist versus Bayesian approaches, design-based versus model-based inference, and specialized frameworks for time-series, causal inference, or survey methodology. In practice, statistikkrammer helps ensure consistency, comparability, and quality control across analyses, while also clarifying the limitations and scope of conclusions. The concept is flexible and can be adapted to different disciplines and data contexts.