Evidential Statistics as a statistical modern synthesis to support 21 st century science . 1

6 During the 20th century, population ecology and science in general relied on two very 7 different statistical paradigms to solve its inferential problems: error statistics (also 8 referred to as classical statistics and frequentist statistics) and Bayesian statistics. A great 9 deal of good science was done using these tools, but both schools suffer from technical 10 and philosophical difficulties. At the turning of the 21st century (Royall, 1997, Lele 11 2004), evidential statistics emerged as a seriously contending paradigm. Drawing on and 12 refining elements from error statistics, likelihoodism, Bayesian statistics, information 13 criteria, and robust methods, evidential statistics is a statistical modern synthesis that 14 smoothly incorporates model identification, model uncertainty, model comparison, 15 parameter estimation, parameter uncertainty, pre-data control of error, and post-data 16 strength of evidence into a single coherent framework. We argue that evidential statistics 17 is currently the most effective statistical paradigm to support 21st century science. 18 Despite the power of the evidential paradigm, we think that there is no substitute for 19 learning how to clarify scientific arguments with statistical arguments. In this paper we 20 sketch and relate the conceptual bases of error statistics, Bayesian statistics and evidential 21 statistics. We also discuss a number of misconceptions about the paradigms that have 22 hindered practitioners, as well as some real problems with the error and Bayesian 23 statistical paradigms solved by evidential statistics. 24