disciplinesstatistics

Disciplinesstatistics is a field that studies statistics as used and produced across academic disciplines. It encompasses how different fields generate data, apply statistical methods, report results, and value statistical evidence. The term is sometimes used interchangeably with discipline-level statistics, statistics of disciplines, or cross-disciplinary statistics.

It draws on methods from bibliometrics, scientometrics, and meta-research to analyze patterns such as publication and citation activity, the prevalence of particular statistical techniques (for example, regression, Bayesian methods, or machine learning), and the availability of data and software. Data sources commonly include scholarly articles, textbooks, curricula, grant records, and repository metadata, often requiring field normalization to compare across disciplines with different publishing norms.

Applications include informing research policy and evaluation, guiding curriculum development, improving reproducibility, and identifying disciplinary gaps in statistical training. Researchers may use clustering, topic modeling, network analysis, and regression to compare disciplinary practices, or to map interdisciplinary connections and the diffusion of statistical ideas.

Challenges include unclear or shifting discipline boundaries, differences in terminology, inconsistent reporting of methods, and biases in data coverage. Normalization across fields is essential but complex, and there is ongoing debate about the interpretation of cross-disciplinary comparisons and the risk of misrepresenting a field’s statistical practices.

Disciplinesstatistics relates to broader fields such as scientometrics, bibliometrics, and meta-research, and it contributes to understanding how statistics shapes and is shaped by different scientific domains.