Which statistical theorem helps estimate probabilities based on evolutionary models?

Bayes' Theorem is fundamental in estimating probabilities based on prior knowledge and evidence, which aligns well with the principles of evolutionary biology. This theorem provides a mathematical framework for updating probabilities as new information becomes available, making it particularly useful in the context of evolutionary models where predictions about species' traits or behaviors can be refined as more genetic or environmental data are gathered.

In evolutionary biology, researchers often use Bayes' Theorem to infer the likelihood of specific evolutionary pathways or adaptations based on observed data and existing theories. By incorporating prior probabilities (which represent previous knowledge or assumptions) with new evidence, scientists can make more informed predictions about evolutionary processes and their outcomes. This integration of existing knowledge and new information is crucial for understanding complex biological systems and their development over time.

The other statistical concepts, while important in their own contexts, do not specifically provide a method for updating probabilities based on evolving models and data in the same way that Bayes' Theorem does.