Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

The Observer has compiled a list of 2025 guides, tutorials, and manuals designed to support psychological scientists as they ...

Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

Statistical Science, Vol. 35, No. 1, Special Issue on Statistics and Science (February 2020), pp. 75-91 (17 pages) Toward the last quarter of the 20th century, the emergence of single-molecule ...

The six-vertex model is a paradigmatic framework in statistical mechanics, originally introduced to capture the essential physics of ice-type systems. By assigning one of six allowed configurations to ...

Indian American statistician to lead journal amid biosciences being transformed by powerful statistical, machine learning and ...

Misuse of statistics in medical and sports science research is common and may lead to detrimental consequences to healthcare. Many authors, editors and peer reviewers of medical papers will not have ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

MIT researchers developed a method that generates more accurate uncertainty measures for certain types of estimation. This could help improve the reliability of data analyses in areas like economics, ...