Estimation methods form the backbone of statistical inference, allowing researchers to infer unknown parameters from observed data. Classical approaches include the maximum likelihood estimator (MLE), ...

Statistical inference comprises the framework by which data are used to draw conclusions about underlying phenomena or populations. At its heart lies hypothesis testing, a procedure that evaluates ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

In the 21st century, artificial intelligence (AI) has emerged as a valuable approach in data science and a growing influence in medical research, 4-6 with an accelerating pace of innovation. This ...

This comprehensive course bridges the gap between foundational statistical reasoning and practical applications related to business and engineering decision-making. Throughout the course, we’ll ...

University of Illinois researchers have developed a method using causal inference for predicting upsets in the NCAA Men's Basketball Tournament that outperforms many other techniques. In addition to ...

Machine learning and artificial intelligence wouldn't be possible without the statistical models that underpin their analytic capabilities. A Cornell statistician and his colleague have developed a ...

This paper describes threats to making valid causal inferences about pandemic impacts on student learning based on cross-year comparisons of average test scores. The paper uses Spring 2021 test score ...

Applied Statistics is the implementation of statistical methods, techniques, and theories to real-world problems and situations in several fields, such as science, engineering, business, medicine, ...