A new simulation approach for the determination of the error of parameters of biological importance describing the growth of the Ehrlich ascites tumor of the mouse.
Some aspects of the growth of the Ehrlich ascites tumor of the mouse can be described by the logistic function. The parameters used are fitted to the experimental data in a nonlinear way. It is important to determine the error of these parameters of biological importance in order to discriminate between different consequences induced by various experimental situations by means of statistics. But often there is a gap between the data that are available and the stronger assumptions necessary for statistics. We propose a simulation approach which also works in a much larger range of similar situations to close this gap.