Ten quick tips to get you started with Bayesian statistics | Ten quick tips to get you started with Bayesian statistics

Bayesian statistics is a framework in which our knowledge about unknown quantities of interest (especially parameters) is updated with the information in observed data, though it can also be viewed as simply another method to fit a statistical model. It has become popular in many branches of biology [1,2]. For context, 5 of the 10 most cited papers in Web of Science with keywords ‘Bayesian statistics’ are related to biology (as of August 19, 2024). The use of Bayesian statistics in biology allows researchers to run analyses that incorporate external knowledge, describe complex systems, and work effectively with limited or messy data. However, most biologists are first trained in frequentist statistics. Learning to become fluent in Bayesian statistics may be perceived as too time-consuming to undertake, or the prospect of adopting an unfamiliar statistical framework can simply appear too daunting. Despite this perception, however, the learning curve for Bayesian statistics is gradual, not steep, and benefits will quickly outweigh investments. To aid you on this journey, we provide a list of 10 tips, summarized in Fig 1, to help you get started with Bayesian statistics. In Table 1 we have also compiled a glossary for definitions of technical terms. Our paper is not intended as a comprehensive introduction to Bayesian statistics. Instead, it provides guidance for applying Bayesian statistics and points to additional resources where you can learn the basics. This paper is not just for newcomers but also for those with some experience in Bayesian methods who may use it as a roadmap to design, conduct, and publish Bayesian analyses. We’ve drawn mainly on our experience teaching and working with ecologists, but we hope these tips will be relevant to a broader audience of biologists. For those seeking to deepen their understanding, we point to more comprehensive resources that offer an in-depth exploration of Bayesian statistics. The purpose of our paper is not to persuade you to abandon frequentist methods in favor of Bayesian methods. Instead, we advocate for a pragmatic dual approach where you master both methods as part of your analytical toolkit and choose the most appropriate tool for your problem.

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