Statistical detection theory

Statistical detection theory is a framework used to determine whether a particular signal or event is present within noisy data. It involves analyzing data to decide between two hypotheses: one where the signal exists and one where it doesn't. By applying probability and statistical methods, the theory helps optimize decision-making to minimize errors—such as false alarms or missed detections. This approach is used in various fields like radar, medical imaging, and communications to reliably identify meaningful signals amidst uncertainty and noise, ensuring decisions are based on rigorous, probabilistic reasoning.