Likelihood

A likelihood treats observed data as fixed and parameters as varying.

Different ways to understand it

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Bayes theorem
Bayes theorem relates conditional probabilities in opposite directions using prior probabilities and a nonzero evidence probability.
Credible intervals
A credible interval contains a specified posterior probability under the Bayesian model.
Linear regression
A response's conditional mean is modeled as linear in coefficients with assumptions about residuals.
Maximum likelihood
Parameters maximize the likelihood of observed data within a specified model.
Analysis of variance
ANOVA partitions variation to test mean differences under model assumptions.