Type I and Type II Error

Making decisions in hypothesis testing always involves some risk of error. A Type I error occurs when we incorrectly reject a null hypothesis that is actually true, meaning we detect an effect that does not exist. A Type II error occurs when we fail to detect a real effect and incorrectly retain the null hypothesis. There is a natural trade-off between these two errors; reducing one may sometimes increase the other. Understanding them is critical in medical testing, model selection, A/B testing, and risk-sensitive decision systems.