Think like a Bayesian and avoid pitfalls from our frequentist past

Abstract

Bayesian inference is a powerful tool that is increasingly being used by ecologists. This is largely due to the flexibility in model specification and improvements in software that makes this tool easier to use. However, with increasing ease of use comes a risk of misuse or abuse. We review four major issues we have identified in the use of Bayesian methods and offer reminders and suggestions that will improve the application and reporting of Bayesian inference while at the same time, hopefully, avoiding the pitfalls that have plagued null hypothesis statistical testing (NHST). These issues include; 1) understanding software and model specification; 2) use of prior probability distributions; 3) maximizing utility of posterior probability distributions; and 4) avoiding dichotomous thinking (i.e., the NHST pitfall). We suggest ecologists should strive for openness in their use of statistical software by understanding their model and providing the full computer code used, develop reasonable and informative priors, and make full use of posterior information that Bayesian methods provide. At the same time, ecologists should avoid dichotomizing results into significant/ non-significant boxes, eliminate null hypothesis tests (including probability intervals for hypothesis testing), and use clear language when describing results. Finally, quantitative training should be expanded in undergraduate curricula to provide students with a larger suite of foundational core concepts that extend beyond NHST.