Probability distributions in library and information science: A historical and practitioner viewpoint

Abstract

This paper has a dual character dictated by its twofold purpose. First, it is a speculative historiographic essay containing an attempt to fix the present position of library and information science within the context of the probabilistic revolution that has been encompassing all of science. Second, it comprises a guide to practitioners engaged in statistical research in library and information science. There are pointed out the problems of utilizing statistical methods in library and information science because of the highly and positively skewed distributions that dominate this discipline. Biostatistics are indicated as the source of solutions for these problems, and the solutions are then traced back to the British biometric revolution of 1865‐1950, during the course of which modern inferential statistics were created. The thesis is presented that science has been undergoing a probabilistic revolution for over 200 years, and it is stated that this revolution is now coming to library and information science, as general stochastic models replace specific, empirical informetric laws. An account is given of the historical development of the counting distributions and laws of error applicable in statistical research in library and information science, and it is stressed that these distributions and laws are not specific to library and information science but are inherent in all biological and social phenomena. Urquhart’s Law is used to give a practical demonstration of the distributions. The difficulties of precisely fitting data to theoretical probability models in library and information science because of the inherent fuzziness of the sets are discussed, and the paper concludes with the description of a simple technique for identifying and dealing with the skewed distributions in library and information science. Throughout the paper, emphasis is placed on the relevance of research in library and information science to social problems, both past and present.