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. For context, five of the ten most cited papers in Web of Science with keywords 'Bayesian statistics' are related to biology (as of August 19, 2024). Bayesian statistics is particularly valuable for biology because it allows researchers to incorporate prior knowledge, handle complex systems, and work effectively with limited or messy data. However, most biologists are trained in frequentist techniques, and the learning curve 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. We provide a list of 10 tips to help you get started with Bayesian statistics. You can also refer to the Glossary for definitions of the technical terms. This paper isn’t just for newcomers; even those with some experience in Bayesian methods may find it a useful 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 in-depth exploration of Bayesian statistics.