Real-life data often contain mixtures of different types of data, which makes the choice of analysis technique somewhat arbitrary. It is quite possible that two statisticians confronted with the same data set may select different methods of data analysis, depending upon what assumptions they are willing to take into account while interpreting the results of analysis. Suppose, there is one dependent variable measured on the interval scale, and five independent variables, of which three are interval-scaled variables, one nominal variable and one ordinal variable with five modalities. In such a situation, some statisticians would use multiple regression analysis, treating one ordinal variable as interval-scale variable and use dummy variables for the nominal variable. Some statisticians may categorize all the interval scale variables and perform an analysis of variance.