Which term describes the ratio of likelihood for one hypothesis over another?

The term that describes the ratio of likelihood for one hypothesis over another is Bayesian factor. The Bayesian factor is a key concept in Bayesian statistics that allows researchers to compare the evidence for competing hypotheses. It quantifies how much more likely the data are under one hypothesis compared to another, essentially providing a measure of support for each hypothesis based on the observed data. This contrasts with traditional hypothesis testing, which focuses on rejecting a null hypothesis; instead, Bayesian factors facilitate a more nuanced understanding of relative evidence.

Effect size typically refers to the magnitude of a relationship or difference, such as Cohen's d, which measures the standardized difference between two means. Statistical power, on the other hand, indicates the probability that a test will correctly reject a false null hypothesis. While these concepts are essential in research, they do not address the relative likelihood of one hypothesis compared to another, which is the specific focus of Bayesian factors.