Pooldus

Pooldus is a term used in theoretical discussions of data integration and statistical analysis to denote a methodological approach that emphasizes combining data from multiple sources to improve the precision and generalizability of estimates.

In practice, pooldus involves identifying compatible data pools, aligning measurement scales, and applying weights to account for differences among sources. Analysts may apply harmonization protocols, choose between fixed-effect or random-effects models, and use meta-analytic techniques to synthesize results. The approach stresses careful assessment of heterogeneity, potential biases, and data quality, as pooling disparate datasets can amplify inconsistencies if not properly managed.

Pooldus is largely discussed in theory and pedagogy rather than as a standard method in applied statistics. It can be useful in clinical research, social science, and environmental studies, where small studies exist or data are available in different formats. However, risks include biased estimates if sources differ systematically, confidentiality and ethical concerns, and misalignment of variables. The term remains exploratory and is not widely adopted as a formal methodology in mainstream practice.

The origin of the term pooldus is uncertain and it is not widely established in formal statistical nomenclature. It appears primarily in theoretical discussions, pedagogical texts, and some online forums as a way to illustrate the concept of data pooling across heterogeneous sources.

See also meta-analysis, data harmonization, data pooling.