Noise in the nervous system is a fundamental challenge for information processing and affects all aspects of nervous system function. Recent studies have shown that multiple noise sources contribute to trial-to-trial variability in both cellular and behavioral responses. This review discusses the sources of noise in the nervous system, from the molecular to the behavioral level, and how noise contributes to variability. It also highlights how noise affects neuronal networks and the principles the nervous system uses to counter its detrimental effects, as well as the potential benefits of noise. Sensory noise arises from the intrinsic noise of external stimuli, such as thermodynamic or quantum mechanical processes. This noise is present at the first stage of perception, where sensory signals are converted into chemical or mechanical signals. Sensory noise can increase trial-to-trial variability and set perceptual thresholds for later stages of information processing. To reduce noise, organisms often pay a high metabolic and structural cost at the sensory stage. Cellular noise is generated by random processes within neurons, such as fluctuations in ion channels and biochemical reactions. This noise can affect the timing and reliability of action potentials (APs) and contributes to variability in neuronal responses. The Fano factor is a measure of variability that ignores temporal structure and higher-order statistics. Some neurons exhibit high variability, while others have low variability, depending on the stimulus conditions. Electrical noise in neurons causes membrane-potential fluctuations and affects AP timing. Channel noise is a dominant source of electrical noise and can significantly alter whole-cell responses. In smaller neurons, the influence of noise is more pronounced, as the number of molecules involved is small. Synaptic noise arises from random events in the synaptic-transmission machinery, such as the spontaneous release of neurotransmitter vesicles. This noise can cause variability in postsynaptic responses and is influenced by factors such as the number of neurotransmitter molecules released per vesicle, the location of vesicle release, and the number of receptor proteins at a synapse. Motor noise is inherent in movement and is influenced by the variability in the force generated by muscle fibers. This variability is affected by factors such as the firing rates of motor neurons and the synchronization of motor neurons through common mechanosensory feedback. The nervous system uses principles such as averaging and prior knowledge to minimize the negative consequences of noise. Averaging can counter noise when multiple units carry the same signal and each is affected by independent sources of noise. Prior knowledge allows the nervous system to distinguish signals from noise and optimize information processing. Noise is an inescapable consequence of the nervous system's molecular components and complex networks. Despite significant noise levels, the brain appears to function reliably, likely due to evolutionary constraints imposed by noise. Understanding the nervous system requires distinguishing variability from noise and appreciating how it influences the brain's structure and function.Noise in the nervous system is a fundamental challenge for information processing and affects all aspects of nervous system function. Recent studies have shown that multiple noise sources contribute to trial-to-trial variability in both cellular and behavioral responses. This review discusses the sources of noise in the nervous system, from the molecular to the behavioral level, and how noise contributes to variability. It also highlights how noise affects neuronal networks and the principles the nervous system uses to counter its detrimental effects, as well as the potential benefits of noise. Sensory noise arises from the intrinsic noise of external stimuli, such as thermodynamic or quantum mechanical processes. This noise is present at the first stage of perception, where sensory signals are converted into chemical or mechanical signals. Sensory noise can increase trial-to-trial variability and set perceptual thresholds for later stages of information processing. To reduce noise, organisms often pay a high metabolic and structural cost at the sensory stage. Cellular noise is generated by random processes within neurons, such as fluctuations in ion channels and biochemical reactions. This noise can affect the timing and reliability of action potentials (APs) and contributes to variability in neuronal responses. The Fano factor is a measure of variability that ignores temporal structure and higher-order statistics. Some neurons exhibit high variability, while others have low variability, depending on the stimulus conditions. Electrical noise in neurons causes membrane-potential fluctuations and affects AP timing. Channel noise is a dominant source of electrical noise and can significantly alter whole-cell responses. In smaller neurons, the influence of noise is more pronounced, as the number of molecules involved is small. Synaptic noise arises from random events in the synaptic-transmission machinery, such as the spontaneous release of neurotransmitter vesicles. This noise can cause variability in postsynaptic responses and is influenced by factors such as the number of neurotransmitter molecules released per vesicle, the location of vesicle release, and the number of receptor proteins at a synapse. Motor noise is inherent in movement and is influenced by the variability in the force generated by muscle fibers. This variability is affected by factors such as the firing rates of motor neurons and the synchronization of motor neurons through common mechanosensory feedback. The nervous system uses principles such as averaging and prior knowledge to minimize the negative consequences of noise. Averaging can counter noise when multiple units carry the same signal and each is affected by independent sources of noise. Prior knowledge allows the nervous system to distinguish signals from noise and optimize information processing. Noise is an inescapable consequence of the nervous system's molecular components and complex networks. Despite significant noise levels, the brain appears to function reliably, likely due to evolutionary constraints imposed by noise. Understanding the nervous system requires distinguishing variability from noise and appreciating how it influences the brain's structure and function.