Noise refers to unwanted variations or interference that corrupt the original signal as it travels through a medium such as air, cable, or fibre. In analogue signals, noise directly alters the waveform, causing distortion that can significantly degrade the quality of the signal. For instance, in audio transmission, this might result in hissing, popping, or static sounds. Since analogue signals have infinite possible values, even small disturbances can make a big difference in the output. Digital signals, however, are more resistant to noise because they consist of only two distinct levels—0 and 1. As long as the signal remains recognisably within the voltage thresholds for each level, it can be accurately interpreted. If the noise causes the signal to drop below a threshold, error detection and correction algorithms can often identify and fix the issue. This robustness is why digital signals are preferred for data transmission over long distances or in environments with significant interference.

Quantisation noise is the small difference between the actual analogue input value and its closest digital approximation during analogue to digital conversion. This occurs because when a continuous range of values is mapped to a limited set of discrete digital levels, some rounding must happen. For example, if an analogue input has a value of 2.83 volts and the nearest representable digital level is 2.75 volts, the system records 2.75 volts instead, creating a small error known as quantisation error. This error introduces quantisation noise into the signal, which may affect the accuracy of the data. The more bits used in the conversion process (i.e. higher sample resolution), the smaller each digital step becomes, and the less noticeable the quantisation noise. In high-fidelity applications like professional audio, minimising quantisation noise is essential to maintain quality, which is why such systems use high-resolution ADCs. In most everyday scenarios, however, the effects are negligible.

Binary is used for digital signals because it aligns perfectly with the physical limitations and reliability of electronic components. Each binary digit (bit) has only two possible values—0 or 1—which can be easily represented using two voltage levels, such as 0 volts for 0 and 5 volts for 1. This makes it easy to distinguish between states and reduces the chance of misinterpretation caused by noise or voltage fluctuations. Using a system with more than two states, such as decimal or hexadecimal, would require multiple voltage levels that are much closer together. This would increase the chances of signal degradation and error due to small variations in voltage. Furthermore, digital circuits such as logic gates and memory elements are simpler, faster, and cheaper to design using binary logic. Although hexadecimal or decimal representations are useful for human readability, all data within the actual hardware is ultimately processed in binary form for maximum efficiency and reliability.

The sampling rate determines how frequently an analogue signal is measured during conversion to a digital format. A higher sampling rate means more samples are taken per second, capturing more detail and allowing the digital version to more closely match the original analogue waveform. According to the Nyquist Theorem, to accurately reproduce a signal without loss of information, the sampling rate must be at least twice the highest frequency present in the original signal. For example, human hearing ranges up to approximately 20 kHz, so CD-quality audio uses a sampling rate of 44.1 kHz. If the sampling rate is too low, a phenomenon called aliasing can occur, where higher frequencies are misrepresented as lower ones, resulting in distortion. In practice, higher sampling rates increase accuracy but also require more storage and processing power. Therefore, systems must balance sampling rate with file size and intended application to achieve the desired fidelity without excessive resource usage.

No, digital systems cannot represent all types of analogue signals perfectly due to fundamental limitations in how analogue data is sampled and stored. The process of analogue-to-digital conversion involves both sampling and quantisation. Sampling means that only specific points in time are recorded, so any changes between those points are not captured. Quantisation further limits accuracy by rounding each sampled value to the nearest available digital level. These two steps inevitably result in a loss of detail compared to the original analogue signal. While using higher sampling rates and greater sample resolutions can reduce this loss, they cannot eliminate it entirely. Moreover, some analogue signals may contain frequency components beyond what the digital system can handle, causing aliasing unless appropriate filtering is used. In practice, though, high-quality digital systems can come extremely close to perfect representation, making the loss imperceptible to human senses. Still, from a theoretical standpoint, complete perfection is unattainable in any finite digital representation.