Significance testing, with appropriate multiple testing correction, is currently the most convenient method for summarizing the evidence for association between a disease and a genetic variant.

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 ...

High-dimensional statistical testing and covariance analysis constitute a rapidly evolving field that addresses the challenges inherent in analysing datasets where the number of variables often ...

Learn the NumPy trick for generating synthetic data that actually behaves like real data.

A test of statistical significance addresses the question, How likely is a result, assuming the null hypotheses to be true. Randomness, a central assumption underlying commonly used tests of ...

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 ...

Given the highly infectious nature of the COVID pathogen, tests for the virus had to be quick, reliable and safe. The test ...

This is a preview. Log in through your library . Abstract A nonparametric statistical test of the performance of ordinations is adapted and extended from the work of Feigin and Cohen (1978). Two ...

This short course will provide an overview of non-parametric statistical techniques. The course will first describe what non-parametric statistics are, when they should be used, and their advantages ...

Let’s start with a definition of Applied Statistics: Applied Statistics is the root of data analysis. The practice of applied statistics involves analyzing data to help define and determine an ...