Replication data for: A Statistical Method for Empirical Testing of Competing Theories

We demonstrate that a well-known class of statistical models, called finite mixture models, provides an effective way of rival theory testing. In the proposed framework, each observation is assumed to be generated from a statistical model implied by one of the competing theories or more generally from a weighted combination of multiple statistical models under consideration.

Researchers can then estimate the probability that a specific observation is consistent with either of the competing theories. By directly modeling this probability with covariates, one can also determine the conditions under which a particular theory applies. We discuss a principled way to identify a list of observations that are statistically significantly consistent with each theory.