Disobeying Power Laws: Perils for Theory and Method

Abstract

The “norm of normality” is a myth that organization design scholars should believe only at their peril. In contrast to the normal (bell-shaped) distribution with independent observations and linear relationships assumed by Gaussian statistics, research shows that nearly every input and outcome in organizational domains is power-law (Pareto) distributed. These highly skewed distributions exhibit unstable means, unlimited variance, underlying interdependence, and extreme outcomes that disproportionally influence the entire system, making Gaussian methods and assumptions largely invalid. By developing more focused research designs and using methods that assume interdependence and potentially nonlinear relationships, organization design scholars can develop theories that more closely depict empirical reality and provide more useful insights to practitioners and other stakeholders.

Keywords: Power-law distributions, Gaussian statistics, Pareto, Nonlinear statistical methods, Theory building