Wed, 06 May 2026 17:47:33 +0000

Why Every Statistician Fails the Ratio Test—Before It’s Too Late In the world of statistical analysis, the Ratio Test is often presented as a crucial tool for comparing nested models—especially in generalized linear models and maximum likelihood estimation. But here’s a surprising truth: every statistician fails the Ratio Test—before it’s too late. Whether through misapplication, overreliance, or misunderstanding, rushing to interpret ratio test results can lead to flawed conclusions, wasted resources, and diminished credibility. ### The Popular Mystique of the Ratio Test The likelihood ratio test (LRT) compares two models: a full model that includes all parameters versus a constrained (simpler) model with reduced parameters. The test statistic, based on the log-likelihood difference, follows a chi-squared distribution under the null hypothesis, enabling formal hypothesis testing.