This chapter presents an in-depth exploration of statistical evaluation methods for stationary processes within the framework of correlation theory, emphasizing the importance of stationarity and ergodicity. It covers diverse approaches to determine the statistical characteristics of stationary random processes including mathematical expectation, variance, autocovariance and autocorrelation functions, power spectral density, and coherence functions. Further, the chapter discusses the conditions for ergodicity with respect to various statistical measures, underlining the significance of confirming stationarity and ergodicity in practical measurement tasks. The research highlights statistical estimation techniques to characterize ergodic processes from measurement data, emphasizing model adequacy and addressing the challenges introduced by digital processing methods. It proposes methods for testing stationarity using F- and t-criteria, focusing on time characteristics like mathematical expectation, variance, and autocorrelation. The evaluation of spectral characteristics using techniques such as Fourier transformation and correlation function analysis is also discussed, with guidelines for sampling and preprocessing to improve the quality of statistical estimates. The chapter concludes by asserting the importance of robust statistical evaluation in digital time series analysis and information measurement technologies, providing a comprehensive framework for accurate modeling and data processing.