Statistical significance is a critical concept in data analysis and research. In essence, it’s a measure that allows researchers to assess whether the results of an experiment or study are due to ...

Successful completion of this course demonstrate your achievement of the following learning outcomes for the MS-DS program: Define a composite hypothesis and the level of significance for a test with ...

Basic concepts in hypothesis testing, including effect sizes, type I and type II errors, calculation of statistical power, non-centrality parameter, and applications of these concepts to twin studies.

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 ...

Multivariate statistical inference encompasses methods that evaluate multiple outcomes or parameters jointly, allowing researchers to understand complex interdependencies within data. Permutation ...

Statistical testing and lower bounds in distributed estimation constitute a rapidly evolving area that addresses both the design of robust tests for assessing data properties across networked systems ...

This paper describes a permutational step-up multiple comparison procedure to adjust the p values from k related hypotheses. The method is applicable when two groups of subjects are being compared on ...