Amalgamation of evidence in statistics is conducted in several ways. Within a study, multiple observations are combined by averaging, or as factors in a likelihood or prediction algorithm. In multilevel modeling or Bayesian analysis, population or prior information is combined with data using the weighted averaging derived from probability modeling. In a scientific research project, inferences from data analysis are interpreted in light of mechanistic models and substantive theories. Within a scholarly or applied research community, data and conclusions from separate laboratories are amalgamated through a series of steps, including peer review, meta-analysis, review articles, and replication studies. These issues have been discussed for many years in the philosophy of science and statistics, gaining attention in recent decades first with the renewed popularity of Bayesian inference and then with concerns about the replication crisis in science. In this article, we review the amalgamation of statistical evidence from different perspectives, connecting the foundations of statistics to the social processes of validation, criticism, and consensus building.