BASIC REQUIREMENTS FOR STATISTICAL METHODS OF DATA ANALYSIS
The article is devoted to the substantiation of the usefulness of developing, discussing and disseminating a system of basic requirements for the development and application of statistical methods for data analysis, for their description in publications, dissertations, etc. For half a century, the author advised scientists of various specialties, reviewed their articles and books, and opposed dissertations. This activity provided an opportunity to get acquainted with hundreds of case studies on the development and application of statistical methods. Various shortcomings have been identified in the conduct of studies and the publication of their results, which hinder their perception, and in some cases cast doubt on the adequacy of the conclusions. Therefore, the author came to the conclusion about the expediency of developing natural requirements for data processing methods and presenting the results of statistical analysis. This article is devoted to the initial consideration of a number of formulations of such requirements. We proceed from the modern paradigm of applied statistics (based on non-parametric and non-numerical statistics), which replaced the primitive paradigm of the 19th century. and the outdated paradigm of the middle of the 20th century, based on the use of parametric distribution systems. When describing and discussing the procedures of statistical analysis, it is necessary to start with probabilistic-statistical models for generating the data under study. An analysis of the diversity of regression analysis models leads to the conclusion that there is no single "standard model". According to measurement theory, the first step in analyzing data is to identify the scales on which they are measured. Statistical inferences must be invariant under allowable transformations of measurement scales. Since the distributions of real data are non-normal, preference should be given to non-parametric methods. The possibility of using parametric families of distributions must be carefully justified. When testing statistical hypotheses, both the null and alternative hypotheses must be specified. It is necessary to study the stability of the conclusions drawn from the model with respect to acceptable changes in the initial data and assumptions of the model. Neural network methods of data analysis are part of applied statistics