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In statistical literature, the Cauchy distribution is often branded as "evil" or "pathological" because it lacks well-defined mean, variance, or any higher-order moments. As a result, it is frequently used as a counterexample to demonstrate the breakdown of certain statistical laws, such as the Central Limit Theorem and the Law of Large Numbers - and has otherwise seen limited practical use. Interestingly, a recent publication by Liu and Xie (2020) uncovers an angelic side of

It's often claimed that analysis of covariance (ANCOVA) is biased or invalid if a nonlinear relationship exists between covariates and...

In his excellent book Statistical Issues in Drug Development  (2021), Professor Stephen Senn provides a comprehensive overview of...

In May 2023, the US Food and Drug Administration (FDA) issued final guidance on adjusting for covariates in randomized clinical trials...

When a clinical trial demonstrates positive efficacy results, there is often interest in conducting various post-hoc analyses, including...

You might have heard that the bootstrap procedure can be seen as an approximation of Bayesian inference. This post presents two examples...

Correlated Bernoulli data are quite common in medical research and clinical trials. In most cases, the correlations between the binary...

Consider a video game played by two teams: 𝑚 boys vs 𝑛 girls. Each player puts $1 on the table (ticket price), then all players start...

Calculating group sequential boundaries involves recursive evaluations of complex multivariate normal integrals. The Markov property of...

A recent LinkedIn post by a fellow statistician caught my attention. The author touted the appealing concept of NNT, which is widely understood, but apparently overlooked a serious statistical problem: the commonly used naive estimator of NNT has an ill-behaved (pathological) distribution, making it particularly challenging to draw valid statistical inferences. Sadly, in my view, the use of NNT in medical research has become another example of a poor practice embraced by many