The accuracy of determining the informative parameters of harmonic signals, such as amplitude, phase difference and initial phases, is essential for the correct functioning of many information and measuring systems. Time sampling and level quantization are two key processes in signal processing that affect the accuracy of determining the informative parameters of harmonic signals. Sampling is the selection of signal values only at certain points in time. The higher the sampling rate, i.e., the greater the number of samples per unit of time, the greater the accuracy of parameter measurement, but the greater the computational complexity. Also, the sampling rate must be high enough to ensure that the signal being analyzed is accurately reproduced. If the sampling rate is too low, aliasing may occur, where a high-frequency signal appears as a low-frequency signal. This can cause errors in detecting harmonics amplitude and phase. Another significant aspect is level quantization, which is the limitation of the numerical values of the signal amplitude. The greater the number of quantization levels, the more precise the results, but this leads to an increase in the amount of computation and memory space required to store the results. In this chapter, it will be considered how time sampling and level quantization affect the accuracy of determining the amplitude, phase difference, and initial phases of harmonic signals. Also, methods for improving the accuracy of measurements under the constraints imposed by these processes on signal processing will be described. This section also presents the structure of the virtual laboratory and the mathematical model underlying it, focusing on the analysis of procedures for measuring informative parameters of harmonic signal models using the Discrete Fourier Transform.