A call for changing data analysis practices: from philosophy and comprehensive reporting to modeling approaches and back

Abstract

Many applied disciplines have recognized problems related to the practice of data analysis within their own communities. Some of them have even declared the existence of a statistical crisis that has raised doubts about findings that were once considered well established. In biological sciences, the recognition of misuse or poor reporting of statistics has only begun to be noticed, and is still far behind other disciplines where reforms are currently being explored. These problems are at least partially related to an unclear understanding of the purpose of the statistical tools or the correct interpretation of statistics themselves (e.g. p-values, confidence intervals, Bayes factors). We consider the ways in which data analysis is taught, performed, and presented in journals to be the main issues. A successful statistical analysis requires both statistical skills and also the ability and willingness to put the statistical results in the context of a particular problem. Here we list some of the issues we think require urgent attention, provide some evidence for misuse and poor reporting practices in the plant-soil sciences, and conclude by offering feasible solutions to both frequentists and Bayesian data analysis paradigms. We do not advocate for one of these paradigms over the other; instead we provide recommendations for the appropriate use of each to answer scientific questions. We also hope this opinion paper gives plant-soil researchers an entry point into the statistical literature to facilitate self-teaching and to properly apply, report, and draw inferences from either the classic frequentist or Bayesian statistical methods.