If the phrase “Bayesian calculus” makes you want to run for the hills, you’re not alone! Bayesian logic can sound intimidating at first, but if you give it a little time, you’ll understand how useful it can be for evaluating the evidence for design in the natural world. On a new episode of ID the Future, biologist Jonathan McLatchie gives us a beginner’s guide to Bayesian thinking and teaches us how it can be used to build a strong cumulative case for intelligent design, as well as how we can use it in our everyday lives.

It is one of the most important formulas in all of probability, and it has been central to scientific discovery for the last two centuries. At its heart, Bayes’s theorem, first developed by 18th century English statistician, philosopher, and minister Thomas Bayes, is a method to quantify the confidence one should have in a particular belief or hypothesis. The process results in a likelihood ratio of a hypothesis being true or false, given the evidence. Here, Dr. McLatchie explains what the theorem is, the components that comprise it, when it would typically be used, and some useful examples of Bayesian reasoning in action. 

Dr. McLatchie shows how Bayesian probability can be applied to the evidence for design in nature. First, he argues that the initial prior probability — the intrinsic plausibility of the hypothesis being true given the background information alone — for the design hypothesis is not low:

In the case of intelligent design and our inferences to design in biology, we have independent reasons, I would contend, to already think that a mind is involved in the origin of our cosmos, including the fine-tuning of the laws and constants of our universe…and the prior environmental fitness of nature.

Secondly, when you add in the evidence we’ve discovered of the complexity of living cells, the infusions of new biological information into the biosphere over time, the evidence for the Big Bang, and more, the cumulative case for intelligent design grows stronger. “If we suppose that a mind is involved,” says McLatchie,

then it’s not hugely improbable that we’d find information content in the cell, and that we’d have information processing systems and that we’d have irreducibly complex machines. But, on the other hand, it is overwhelmingly improbable, I would argue, that such information-rich systems and irreducibly complex machinery would exist on the falsity of the design hypothesis. And so you have this overwhelmingly top-heavy likelihood ratio.

Download the podcast or listen to it here.

Dig Deeper

Read the article series by Dr. McLatchie that inspired this episode:

• “A Bayesian Approach to Intelligent Design“
• “Applying Bayes’ Theorem to Biological Design”
• “The Advantages of a Bayesian Approach to ID“

Read Dr. Lydia McGrew’s paper on a Bayesian approach to ID, referenced by Dr. McLatchie in this discussion:

• “Testability, Likelihoods, and Design” (2004)