Concerns over reproducibility in research has reinvigorated the discourse on P-values as measures of statistical evidence. In a position statement by the American Statistical Association board of directors, they warn of P-value misuse and refer to the availability of alternatives. Despite the common practice of comparing P-values across different hypothesis tests in genetics, it is well-appreciated that P-values must be interpreted alongside the sample size and experimental design used for their computation. Here, we discuss the evidential statistical paradigm (EP), an alternative to Bayesian and Frequentist paradigms, that has been implemented in human genetics studies. Using applications in Cystic Fibrosis genetic association analyses, and describing recent theoretical developments, we review how to measure statistical evidence using the EP in the presence of covariates, model misspecification, and for composite hypotheses. Novel graphical displays are presented, and software for their computation is highlighted. The implications of multiple hypothesis testing for the EP are delineated in the analyses, demonstrating a view more consistent with scientific reasoning; the EP provides a theoretical justification for replication that is a requirement in genetic association studies. As genetic studies grow in size and complexity, a fresh look at measures of statistical evidence that are sensible amid the analysis of big data are required.
Keywords: foundations of statistics; inference; likelihood paradigm; multiple hypothesis testing; statistical evidence.
© 2018 The Authors. Genetic Epidemiology Published by Wiley Periodicals, Inc.