Helping biologists get back to biology

These days, when studying biology, about 90% of our job seems to be doing stats (or thinking about stats, or Googling stats questions). The problem is, we’re woefully underprepared for this reality in undergraduate biology programs and often need to spend an inordinate amount of time self-teaching in grad school. The problem is compounded by the fact that most established professors know very little about coding and dealing with the complexities of modern day statistics (ever been told by a supervisor to run a t-test even after explaining that your data don’t meet the assumptions?). Students, post-docs, and ECRs are often left having to figure things out on their own by piecing together bits and pieces of things they’ve heard in class or learned from Stack Exchange.

Bayes' Baes was created to help centralize the information necessary to do stats for biology and reduce the need for frustrated Googling while running analyses. We cover a range of topics from basic coding and data cleaning skills to complex hierarchical analyses, helping you with every step of your project. While this resource will hopefully be useful for anyone looking to sharpen up their statistical and programming skills, it has been specifically collated to help grad students in ecology with the common pitfalls we face when working with data that rarely fit the assumptions of simple analyses.

Hell no! While we hope our tutorials and resources will make Bayesian stats less intimidating, this site covers all sorts of frequentist stats as well.

If we don't have what you're looking for in our own collection of tutorials, there's a good chance we have something helpful on our Resources page, so check that out first! If you still can't find what you're looking for, get in touch via Twitter, email, or through our contact form to make a request.

First of all, rude. But in all seriousness, that's a very fair question! We're not statisticians - and will never claim to be - but we think that's what makes this site useful. Instead of digging too deep into the math and theory behind the stats, we focus on how to better understand and use the most common tools in ecology. As ecologists, we know what it's like to have imperfect data that violate all sorts of model assumptions, so you'll get more practical advice than what a true statistician would provide. And we and our Guest Baes have actually used the tools we write about in our own work with our own messy data, so we can help you with the common pitfalls!