Standard errors are one of the most overlooked pieces of statistical output. They sit underneath confidence intervals, p-values, and claims about precision in almost every study. But what if those standard errors are wrong from the start? ...

Incidence rates are one of the most common measures in epidemiology. They are used to describe how quickly disease is appearing in a population and to compare risk across groups. But what if the rate looks correct while the underlying time at r...

Benchmark accuracy is one of the most trusted signals in machine learning. It tells you which model performs best—and it often drives decisions about what gets deployed. But what if that number is giving you a false sense of confidence? 

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...