As a step toward this resolution, this article looks at the question of what properties statistical inferences might reasonably be expected to have and argues that the use of p-values should be ...

At a recent conference on Bayes, fiducial and frequentist inference, David Cox presented eight illustrative examples, chosen to highlight potential difficulties for the theory of inference. We discuss ...

Specialization: Data Science Foundations: Statistical InferenceInstructor: Dr. Jem Corcoran, Associate Professor in Applied MathematicsPrior knowledge ...

The course provides a comprehensive coverage of fundamental aspects of methods and principles in probability and statistics, as well as linear regression analysis. Real data illustrations with the ...

Challenges at the interface of medical statistics and AI are population inference vs. prediction, generalizability, reproducibility and interpretation of evidence, and stability and statistical gua ...

An important and relatively neglected design issue is how to account for the loss of power from missing data in statistical inferences such as hypothesis tests or confidence intervals.

What is the difference between representative samples and random samples, and how are they are used to reduce sampling bias?

The course will provide a comprehensive coverage on some fundamental aspects of probability and statistics methods and principles. It also covers linear regression analysis. Data illustration using ...