Creation of methods for determining the degree of closeness of a given pseudorandom sequence to a random distribution is a very relevant task in many fields where pseudorandom numbers are used. For example, in cryptography it is important that pseudorandom sequences are unpredictable and have the properties of random numbers. Methods for determining proximity to a random distribution help in assessing the quality of such sequences and identifying flaws in them. In scientific research and engineering projects, pseudorandom numbers are used to model complex systems and simulate random phenomena. It is important to have a means of determining how well such numbers reproduce a random process. In statistics, it is important that pseudorandom sequences meet the requirements of various statistical properties. Methods of determining proximity to a random distribution help in evaluating the properties of these sequences. In software engineering and other fields, it is important to test the quality of pseudorandom number generators to avoid vulnerabilities and flaws in programs. Thus, the development and improvement of methods for determining the degree of closeness of pseudorandom sequences to a random distribution is an urgent task with important practical applications.