likelihoodcentered

Likelihoodcentered is a term that refers to a philosophical or methodological approach that places a strong emphasis on the concept of likelihood in statistical inference and decision-making. At its core, this perspective posits that the probability of observing the data given a particular hypothesis or model, known as the likelihood, is the most crucial piece of information for drawing conclusions. This contrasts with approaches that might prioritize prior beliefs (Bayesian) or direct probability statements about hypotheses (frequentist p-values).

In practice, a likelihood-centered approach involves analyzing the likelihood function, which quantifies how well different parameters or hypotheses explain the observed data. The parameter values or hypotheses that maximize the likelihood are generally considered the most plausible. This principle is fundamental to maximum likelihood estimation (MLE), a widely used statistical technique. MLE seeks to find the parameter values that make the observed data most probable.

The likelihood-centered viewpoint is valued for its objectivity and its ability to provide a unified framework for model comparison and parameter estimation. It focuses on the evidence provided by the data itself, minimizing reliance on subjective prior probabilities. However, it can also be criticized for not fully incorporating prior knowledge or for potential issues with confidence intervals derived solely from likelihood profiles. Despite these considerations, the concept of likelihood remains a cornerstone of modern statistical theory and practice.