A summary of the given content is as follows: The article presents a method for the collective processing of pharmacological series experiments, developed and applied at the Pharmacological Institute of the University of Leipzig. The author, G. Kärber, discusses the application of collective measurement theory in pharmacological experiments, referencing previous works by Trevan, Wiechowski, Behrens, and others. The method aims to handle data from series experiments, particularly when there is significant variability in results. The author emphasizes the need for a computational approach that can yield useful results even in cases of high variability, such as when the response of animals to a drug varies depending on the dose. The method is designed to be applicable both for arithmetic and geometric progression of doses. The author also addresses the issue of negative values that may arise when calculating differences between groups, and decides to avoid such calculations for the arithmetic mean. The formula proposed is (aM) = Dm - [∑(z·d)/m], where (aM) is the arithmetic mean, Dm is the dose at which all animals react, z is half the sum of animals reacting at two consecutive doses, d is the difference between the numbers of animals reacting at two consecutive doses, and m is the number of animals in each group. The method is illustrated with an example. The article highlights the importance of using a systematic and computationally efficient approach for analyzing pharmacological data.A summary of the given content is as follows: The article presents a method for the collective processing of pharmacological series experiments, developed and applied at the Pharmacological Institute of the University of Leipzig. The author, G. Kärber, discusses the application of collective measurement theory in pharmacological experiments, referencing previous works by Trevan, Wiechowski, Behrens, and others. The method aims to handle data from series experiments, particularly when there is significant variability in results. The author emphasizes the need for a computational approach that can yield useful results even in cases of high variability, such as when the response of animals to a drug varies depending on the dose. The method is designed to be applicable both for arithmetic and geometric progression of doses. The author also addresses the issue of negative values that may arise when calculating differences between groups, and decides to avoid such calculations for the arithmetic mean. The formula proposed is (aM) = Dm - [∑(z·d)/m], where (aM) is the arithmetic mean, Dm is the dose at which all animals react, z is half the sum of animals reacting at two consecutive doses, d is the difference between the numbers of animals reacting at two consecutive doses, and m is the number of animals in each group. The method is illustrated with an example. The article highlights the importance of using a systematic and computationally efficient approach for analyzing pharmacological data.