Confidence intervals are everywhere in research. They are supposed to show uncertainty, improve interpretation, and give more context than a single point estimate. But what if confidence intervals are creating a false sense of certainty instead...

Screening programs are often seen as one of the clearest wins in public health. Find disease earlier, intervene sooner, and improve outcomes. But what if some screening programs only appear effective because of bias, overdiagnosis, and misleadi...

Cross-validation is one of the most common tools in machine learning.It is supposed to give you a reliable estimate of how your model will perform.But what if that estimate is quietly misleading you?In this episode, we break...

Regression adjustment is one of the most common tools in biostatistics and health research. It is often treated as proof that a study has properly controlled for differences and moved closer to the truth. But what if regression adjustment is cr...

Confounding adjustment is one of the most common phrases in epidemiology and observational research. It is often treated as proof that a study has handled bias and moved closer to a causal answer. But what if adjustment is creating more confide...