The basis for signal detection theory is that virtually all reasoning and decision making revolves around some aspect of uncertainty. It examines an organism's sensory organs to decipher precise sources and to define how living creatures detect signals. The approach of signal detection theory is to quantify the ability to discern between information-based patterns and chaotic patterns that distract from the information.
According to the signal detection theory, there are several determiners of how an organism detects a signal and where its threshold levels take place. Signal detection theory offers a specific language and graphic notation for analyzing decision making in the face of uncertainty. The theory explains how organisms detect signals that affect their ears, eyes, nose, skin and other sensory organs.
Signal detection theory is a statistical measure designed to pinpoint a signal against a background of noise. The theory has direct applications regarding sensory analysis or experiments, along with a means to analyze different kinds of decision problems.
At its core, signal detection theory analyzes two possible states for a signal: absent or present. These states are influenced by the source of stimulation, the detector, and the background noise level. For example, detecting noise in an empty room is easier than detecting noise in a crowded restaurant.