The paper presents the basic theory of complementary statistics and its application in the area of applied probabilistic modeling. By introduction of the complementarity's principle between x-representation (random time series, random process) and p-representation or k-representation (rate of change/velocity of random time series and processes) the probability theory is completed for the "structural" parameter which carries information about the changes of studied time series or the random process. At the end, the basic application of probabilistic modeling is introduced and the presented principle is illustrated on the set of numerical examples with different probability density functions.